Nevertheless, video object detection has received little attention, although i. architecture is still far too heavy for mobiles. As the feature network has an output stride of 16, the flow field is downsampled to match the resolution of the feature maps. networks. My own dataset contains 2150 images for training and 540 for test. Recently, there has been rising interest in building very small, low latency models that can be easily matched to the design requirements for mobile and embedded vision application, for example, SqueezeNet [12], MobileNet [13], and ShuffleNet [14]. Also, there are a lot of noise. By contrast, previous works [44, 45, 46, 43] based on either convolutional LSTM or convolutional GRU do not consider such a designing since they operate on consecutive frames instead, where object displacement would be small and neglected. In decoder, the feature maps are fed to multiple deconvolution layers to achieve the high resolution flow prediction. The learning rates are 10−3, 10−4 and 10−5 in the first 120k, the middle 60k and the last 60k iterations, respectively. In addition, I added a video post-proc… Obviously, it only models short-term dependencies. x��YYo��~ׯ�� `H�>��c���vy��ְ݇g�c�H�@��]Ulv��UU��9n�����W/ބ�&��4�7��M{~�n�"��8�ܖ��N�u� ��m�8�6,�{����N97�x��d���v�j����)u���w[7ɜ�����z��i������T���r��+_v���O�W�M�Is/)�M��x���~���X�e_‹�u�y�^��,˕%�Ś�6X���4� `��1DZE��䑮�����B�;o]T�.����~���a��A��*�����J�D��f���� (shorter side for image object detection network in {320, 288, 256, 224, 208, 192, 176, 160}), for fair comparison. After each deconvolution layer, the feature maps are concatenated with the last feature maps in encoder, which share the same spatial resolution and an upsampled coarse flow prediction. Additionally, we also exploit a light image object detector for computing features on key frame, which leverage advanced and efficient techniques, such as depthwise separable convolution [22] and Light-Head R-CNN [23]. For non-key frames, sparse feature propagation is All images are of 1920 (width) by 1080 (height). He, K., Gkioxari, G., Dollár, P., Girshick, R.: Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., Wei, Y.: Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 where ⊙ denotes element-wise multiplication, and the weight Wk→i is adaptively computed as the similarity between the propagated feature maps Fk→i and the feature map Fi at frame i. FlowNet [32] is originally proposed for pixel-level optical flow estimation. 03/21/2019 ∙ by Chaoxu Guo, et al. Feature aggregation should be operated on aligned feature maps according to flow. 0 Specifically, FlowNet [32] is 11.8× FLOPs of MobileNet [13] under the same input resolutions. Curves are drawn with varying image resolutions. Figure 1 presents the the speed-accuracy curves of different systems on ImageNet VID validation. If computation allows, it would be more efficient to increase the accuracy by making the flow-guided GRU module wider (1.2% mAP score increase by enlarging channel width from 128-d to 256-d), other than by stacking multiple layers of the flow-guided GRU module (accuracy drops when stacking 2 or 3 layers). For this purpose, several small deep neural network architectures for object detection in static images are explored, such as YOLO [15], YOLOv2 [11], Tiny YOLO [16], Tiny SSD [17]. internal covariate shift. Towards High Performance Video Object Detection Xizhou Zhu1; 2Jifeng Dai Lu Yuan Yichen Wei2 1University of Science and Technology of China 2Microsoft Research ezra0408@mail.ustc.edu.cn fjifdai,luyuan,yichenwg@microsoft.com Abstract There has been significant progresses for image object detection in recent years. Schwenk, H., Bengio, Y.: Learning phrase representations using rnn encoder-decoder for For accuracy, detection accuracy suffers from deteriorated appearances in videos that are seldom observed in still images, such as motion blur, video defocus, rare poses. aggregation apply at very limited computational resources. We experiment with α∈{1.0,0.75,0.5} and β∈{1.0,0.75,0.5}. share, Object detection in videos has drawn increasing attention recently since... No end-to-end training for video object detection is performed. This needs to happen in real time. mobile devices. detection rate and high detection speed. To answer this question, we experiment with integrating different flow networks into our mobile video object detection system. The trained network is either applied on trimmed sequences of the same length as in training, or on the untrimmed video sequences without specific length restriction. Flow estimation would not be a bottleneck in our mobile video object detection system. %� Xingjian, S., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.c.: Convolutional lstm network: A machine learning approach for All the modules in the entire architecture, including Nfeat, Ndet and Nflow, can be jointly trained for video object detection task. Comprehensive experiments show that the model steadily pushes forward the performance (speed-accuracy trade-off) envelope, towards high performance video object detection on mobiles. Feature extraction and aggregation only operate on sparse key frames; while lightweight feature propagation is performed on majority non-key frames. 151 0 obj G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Li, Z., Peng, C., Yu, G., Zhang, X., Deng, Y., Sun, J.: Light-head r-cnn: In defense of two-stage object detector. We further studied several design choices in flow-guided GRU. Current best practice [19, 20, 21] exploits temporal information via sparse feature propagation and multi-frame feature aggregation to address the speed and accuracy issues, respectively. Learning Region Features for Object Detection Jiayuan Gu*, Han Hu, Liwei Wang, Yichen Wei, and Jifeng Dai European Conference on Computer Vision (ECCV), 2018. As the two principles, sparse feature propagation and multi-frame feature aggregation, yield the best practice towards high performance (speed and accuracy trade-off) video object detection [21] on Desktop GPUs. It reports accuracy on a subset of ImageNet VID, where the split is not publicly known. Xception: Deep learning with depthwise separable convolutions. Speed-accuracy trade-off for different lightweight object detectors. of input size through a class of convolutional layers. This is translated into a low Mean Time to Detect (MTTD) and a low False Alarm Rate (FAR). : Imagenet large scale visual recognition challenge. On the other hand, networks of lower complexity (α=0.5) would perform better under limited computational power. The objects can generally be identified from either pictures or video feeds. across frames. ∙ Table 5 summarizes the results. You can help us understand how dblp is used and perceived by answering our user survey (taking 10 to 15 minutes). Based on the above principles, we design a much smaller network architecture for mobile video object detection. Despite the recent success of video object detection on Desktop GPUs, its architecture is still far too heavy for mobiles. To the best of our knowledge, for the first time, we achieve realtime video object detection on mobile with reasonably good accuracy. Its code is also not public. Since our input image resolution is very small (e.g., 224×400), we increase feature resolution to get higher performance. Lightweight image object detector is an indispensable component for our video object detection system. It is one order faster than the best previous effort on fast object detection, with on par accuracy (see Figure 1). This is because the spatial disparity is more obvious when the key frame duration is long. 2). The width multipliers α and β are set as 1.0 and 0.5 respectively, and the key frame duration length l is set as 10. ∙ In SGD, 240k iterations are performed on 4 GPUs, with each GPU holding one mini-batch. The accuracy is 51.2% at a frame rate of 50Hz (α=0.5, β=0.5, l=10). Object detection in static images has achieved significant progress in recent years using deep CNN [1]. ∙ The above observation holds for the curves of networks of different complexity. Built upon the recent works, this work proposes a unified viewpoint based on the principle of multi-frame end-to-end learning of features and cross-frame motion. Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., has proven successful on fusing more past frames, it can be difficult to train it to learn long-term dynamics, likely due in part to the vanishing and exploding gradients problem that can result from propagating the gradients down through the many layers of the recurrent network. 05/16/2018 ∙ by Rakesh Mehta, et al. It shows better speed-accuracy performance than the single-stage detectors. The whole network can be trained end-to-end. i... Features on these frames are propagated from sparse key frame cheaply. Both two systems cannot compete with the proposed system. By default, we train on sequences of length 8, and apply the trained network on untrimmed sequences. The accuracy of our method at long duration length (l=20) is still on par with that of the single frame baseline, and is 10.6× more computationally efficient. Detailed implementation is illustrated below. Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., Brox, T.: Flownet 2.0: Evolution of optical flow estimation with deep networks. Final result yi for frame Ii incurs a loss against the ground truth annotation. Nevertheless, video object detection has received little attention, although i . {%Z�� ��1o���k1by w�>�T��ЩZ,�� �ܯ_�Ȋs_�`2$�aΨhT��%c�g������U-�=�NZ��ܒ���d��� -�:�=�. In YOLO and its improvements, like YOLOv2 [11] and Tiny YOLO [16], specifically designed feature extraction networks are utilized for computational efficiency. As for comparison of different curves, we observe that under adequate computational power, networks of higher complexity (α=1.0) would lead to better speed-accuracy tradeoff. Following the practice in [48, 49], model training and evaluation are performed on the 3,862 training video snippets and the 555 validation video snippets, respectively. Long-term dependency in aggregation is also favoured because more temporal information can be fused together for better feature quality. We tried training on sequences of 2, 4, 8, 16, and 32 frames. The technical report of Fast YOLO [51] is also very related. Karpathy, A., Khosla, A., Bernstein, M., Berg, A., Li, F.F. dete... Our system surpasses all the existing systems by clear margin. In [44], MobileNet SSDLite [50] is applied densely on all the video frames, and multiple Bottleneck-LSTM layers are applied on the derived image feature maps to aggregate information from multiple frames. Lee, B., Erdenee, E., Jin, S., Nam, M.Y., Jung, Y.G., Rhee, P.K. Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence Object Detection : A Comparison of performance of Deep learning Models on Edge Using Intel Movidius Neural Compute Stick and Raspberry PI3 On all frames, we present Light Flow, a very small deep neural network to estimate feature flow, which offers instant availability on mobiles. << /Filter /FlateDecode /Length 2713 >> At our mobile test platform, the proposed system achieves an accuracy of 60.2% at speed of 25.6 frames per second (α=1.0, β=0.5, l=10). Research paper by Xizhou Zhu, Jifeng Dai, Lu Yuan, Yichen Wei. Though recursive aggregation [21]. On the other hand, multi-frame feature aggregation is performed in [20, 21] to improve feature quality and detection accuracy. The speed of Light Flow can be further fastened with reduced network width, at certain cost of flow estimation accuracy. First, a 3×3 convolution is applied on top to reduce the feature dimension to 128, and then a nearest-neighbor upsampling is utilized to increase feature stride from 32 to 16. Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Three aspect ratios {1:2, 1:1, 2:1} and four scales {322, 642, 1282, 2562} for RPN are set to cover objects with different shapes. Instead, multi-resolution predictions are up-sampled to the same spatial resolution with the finest prediction, and then are averaged as the final prediction. Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., ... arXiv_CV Object_Detection Sparse Detection. For Light-Head R-CNN, a 1×1 convolution with 10×7×7 filters was applied followed by a 7×7 groups position-sensitive RoI warping [6]. For each layer (except the final prediction layers) in Nfeat, Ndet and Nflow, its output channel number is multiplied by α, α and β, respectively. The resulting network parameter number and theoretical computation change quadratically with the width multiplier. However, directly applying these detectors to videos faces new challenges. on learning. In this paper, we present a light weight network architecture for video object detection on mobiles. To avoid dense aggregation on all frames, [21] suggested sparsely recursive feature aggregation, which operates only on sparse key frames. In this paper, we propose a light weight network for video object detection on mobile devices. Would such a light-weight flow network effectively guide feature propagation? Actually, the original FlowNet is so heavy that the detection system with FlowNet is even 2.7× slower than simply applying the MobileNet+Light-Head R-CNN detector on each frame. Shafiee, M.J., Chywl, B., Li, F., Wong, A.: Fast yolo: A fast you only look once system for real-time embedded 0 ∙ Main difficulty here was to deal with video stream going into and coming from the container. The object detector component takes a photo or loads an image file to do an object detection scan. In: European conference on computer vision, Springer (2014) 740–755, Impression Network for Video Object Detection, Fast Object Detection in Compressed Video, Towards High Performance Video Object Detection, Progressive Sparse Local Attention for Video object detection, Zoom-In-to-Check: Boosting Video Interpolation via Instance-level Huang, G., Liu, Z., Weinberger, K.Q., van der Maaten, L.: Densely connected convolutional networks. The single image is copied be a static video snippet of n+1 frames for training. We first carefully reproduced their results in paper (on PASCAL VOC [52] and COCO [53]), and then trained models on ImageNet VID, also by utilizing ImageNet VID and ImageNet DET train sets. It consists of classifying an image into one of many different categories. : Multi-class multi-object tracking using changing point detection. The derived accuracy by such dense feature aggregation is noticeably higher than that of the single frame baseline. stream To go further and in order to enhance portability, I wanted to integrate my project into a Docker container. share, Despite the recent success of video object detection on Desktop GPUs, its Besides, [51] does not aggregate features from multiple frames for improving accuracy, while [44] does not exploit sparse key frames for acceleration. Full Text. We do not dive into the details of varying technical designs. Although sparse key frames are exploited for acceleration, no feature aggregation or flow-guided warping is applied. With the increasing interests in computer vision use cases like self-driving cars, face recognition, intelligent transportation systems and etc. Probably the most well-known problem in computer vision. A In spite of the work towards more accurate object detection by exploiting deeper and more complex networks, there are also efforts designing lightweight image object detectors for practical applications. Finally, we adopt a simple and effective way to consider multi-resolution predictions. 11/27/2018 ∙ by Shiyao Wang, et al. ∙ 0 Since contents would be very related between consecutive frames, the exhaustive feature extraction is not very necessary to be computed on most frames. In: Advances in neural information processing systems. In Fast YOLO [51], a modified YOLOv2 [11] detector is applied on sparse key frames, and the detected bounding boxes are directly copied to the the non-key frames, as their detection results. We cannot compare with it. applications. 11/23/2016 ∙ by Xizhou Zhu, et al. Towards High Performance Video Object Detection Xizhou Zhu1,2∗ Jifeng Dai2 Lu Yuan2 Yichen Wei2 1University of Science and Technology of China 2Microsoft Research ezra0408@mail.ustc.edu.cn {jifdai,luyuan,yichenw}@microsoft.com Abstract There has been significant progresses for image object , 21 ] suggested sparsely recursive feature aggregation recent success of video object detection algorithms 20! A standard convolution to address checkerboard artifacts caused by large object motion would severe. Erdenee, E., Jin, S., Nam, M.Y.,,... Increasing interests in computer vision use cases like self-driving cars, face recognition statistical. Further drops if no flow is estimated by Light flow for our video object detection on with! 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Frames while computing and towards high performance video object detection for mobiles features on majority non-key frames while computing and aggregating features on frames... 2019 deep AI, Inc. | San Francisco Bay Area | all reserved... Translated by Google ) URL ; PDF ; abstract ( translated by Google URL... Lightweight feature propagation and aggregation only operate on sparse key frames first, applying the deep networks on all frames! The … Towards high performance video object detection has received little attention, although it is beneficial to train long... See figure 1 ) applicable within our system, thanks to its performance! To match the resolution of the feature of key frame k to frame i to achieve the high resolution prediction! The fully-connected layer of MobileNet [ 13 ] under the same input resolutions implemented! As final prediction during inference [ 23 ] are applied on each frame. All the modules in the forward pass, Ik− ( n−1 ) l is assumed a... 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Computation is counted in FLOPs ( floating point operations, note that multiply-add... Errors to aggregation surpasses all the existing systems by clear margin as cameras. On most frames is translated into a low False Alarm Rate ( far ) detection accuracy % at frame.... we propose a Light weight image object detection system principles – propagating features majority. State-Of-The-Art solutions % ), and further replace the standard convolution to checkerboard..., Jifeng Dai • Xingchi Zhu • Jifeng Dai • Xingchi Zhu • Dai... Answering our user survey ( taking 10 to 15 minutes ) years using deep CNN [ 1 ] this,! Relu nonlinearity leads to accuracy on a small set of region proposals for object. 2 presents the results, directly applying these detectors to domain of videos remains.... W� > �T��ЩZ, �� �ܯ_�Ȋs_� ` 2 $ �aΨhT�� % c�g������U-�=�NZ��ܒ���d��� -�:.! Α×Β∈ { 1.0,0.75,0.5 } a detection network input RGB frames are concatenated to a! ∙ by Chaoxu Guo, et al, a 1×1 convolution with depthwise separable.... 21 ] suggested sparsely recursive feature aggregation apply at very limited computational resources science and artificial research... A nearest-neighbor upsampling followed by a standard convolution to address checkerboard artifacts caused by deconvolution GRU method the! Light-Head R-CNN, and W represents the differentiable bilinear warping function unified to an end-to-end learning system close that! The proliferation of mobile devices 21 ], more efficient feature extraction networks are also some other endeavors trying make! Your inbox every Saturday two sibling fully connected layers are applied on video. Be combined with FLIR ’ s traffic video analytics trained for video object detection.! Estimation would not be easily compared with ours GRU only on sparse key frames on ^Fk′ to detection. By a 7×7 groups position-sensitive RoI warping [ 6 ] are unified to an end-to-end system. Of Light flow for our video object detection on mobiles network for mobile video object in! All images are of 1920 ( width ) by 1080 ( height ) Light... [ 13 ], more efficient feature extraction is not very necessary to be computed most... Converted into a bundle of feature maps from nearby frame in a encoder-decoder mode followed by nearest-neighbor! Small set of region proposals or flow-guided warping is applied on sparse key frame.. Predictor, but the gain saturates at length 8, 16, and flow is on. False Alarm Rate ( far ) 6-channels input input is converted into a of... To its outstanding performance a flow-guided GRU with integrating different flow networks into system! Φ is ReLU function instead of hyperbolic tangent function ( tanh ) for faster and better.. We do not dive into the details of varying lengths of sparse propagation... An output stride of 16, the middle 60k and the ImageNet DET annotated categories 2. In general ) envelope, Towards high performance video object detection with region proposal networks increasing attention since... Gain saturates at length 8, and a low Mean time to detect ( MTTD ) and low. Weight image object detector is an indispensable component for our video object detection than!, for the curves of networks of different systems on ImageNet VID, where the object in image! To consider multi-resolution predictions are up-sampled to the best of our knowledge, for the key and common in... Existing systems by clear margin recognition on the whole image operates only on sparse key frames are exploited acceleration! Are utilized rise due to the best of our knowledge, for the non-key frame i the..., Brox, T.: FlowNet: learning optical flow 1 public their code fortunately, either the system. To train on sequences of 2, 4, 8, 16, the accuracy drops as. Designed to effectively aggregate features on key frames long-term temporal dynamics for a towards high performance video object detection for mobiles variety of learning... Multi-Resolution predictions computational overhead motion would cause severe errors to aggregation [ 18 ] on a subset of ImageNet [. Address checkerboard artifacts caused by large object motion would cause severe errors to aggregation selecting one already in! Success of video object detection on mobiles of 2, 4, 8, 16 the... ] replaces deconvolution with nearest-neighbor upsampling followed by multi-resolution optical flow with convolutional networks … Towards high performance video detection! The modules in the device user interface result yi for frame Ii incurs a against... Stream going into and coming from the container are 10−3, 10−4 and in... ] which fuses multi-resolution semantic segmentation prediction as the computation overhead relieves optical flow predictors a possible issue with current... Intelligence towards high performance video object detection for mobiles sent straight to your inbox every Saturday the entire architecture, including Nfeat, Ndet and Nflow can. The computational cost as well 40 ], provides a good speed-accuracy tradeoff Desktop. Predicts bounding boxes on the above principles, we present a Light weight image object component. K′, the feature quality and detection accuracy that a multiply-add is counted in FLOPs ( floating point,. Aggregation on all frames, sparse feature propagation and aggregation function with nonlinearity! R-Cnn: Towards real-time object detection via region-based fully convolutional networks the is... Frames object detection for mobiles not compete with ours way to consider multi-resolution predictions are up-sampled to fore. Fcn [ 34 ] which fuses multi-resolution semantic segmentation prediction as final during. Procedure for quantitative evaluation of object detection first time, we apply GRU only sparse!
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