(2015) Software available from tensorflow.org. The difference is flow-guided GRU is applied. share, We propose a light-weight video frame interpolation algorithm. Object detection is a computer vision technique whose aim is to detect objects such as cars, buildings, and human beings, just to mention a few. Instead, multi-resolution predictions are up-sampled to the same spatial resolution with the finest prediction, and then are averaged as the final prediction. We verified that the principals of sparse feature propagation and multi-frame feature aggregation also hold at very limited computational overhead. Xizhou Zhu, Jifeng Dai, Xingchi Zhu, Yichen Wei, Lu Yuan Despite the recent success of video object detection on Desktop GPUs, its architecture is still far too heavy for mobiles. ∙ Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., Brox, T.: Flownet 2.0: Evolution of optical flow estimation with deep networks. The proposed system In YOLO and its improvements, like YOLOv2 [11] and Tiny YOLO [16], specifically designed feature extraction networks are utilized for computational efficiency. Shafiee, M.J., Chywl, B., Li, F., Wong, A.: Fast yolo: A fast you only look once system for real-time embedded Simple Baselines for Human Pose Estimation and Tracking, ECCV 2018 Bin Xiao, Haiping Wu, Yichen Wei arXiv version Code. In this paper, we propose an efficient and fast object detector which ca... Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., Fischer, We cannot compare with it. Abstract: There has been significant progresses for image object detection in recent years. As the feature network has an output stride of 16, the flow field is downsampled to match the resolution of the feature maps. ∙ When applying Light Flow for our method, to get further speedup, two modifications are made. modeling. formance (speed-accuracy trade-o ) envelope, towards high performance video object detection on mobiles. RPN [5] and Light-Head R-CNN [23] are applied on the shared 128-d feature maps. They can be mainly classified into two major branches: lightweight image object detectors making the per-frame object detector fast, and mobile video object detectors exploiting temporal information. : Imagenet large scale visual recognition challenge. 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. ∙ 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). Huang, G., Liu, Z., Weinberger, K.Q., van der Maaten, L.: Densely connected convolutional networks. To avoid dense aggregation on all frames, [21] suggested sparsely recursive feature aggregation, which operates only on sparse key frames. Faster r-cnn: Towards real-time object detection with region proposal Flow estimation would not be a bottleneck in our mobile video object detection system. Karpathy, A., Khosla, A., Bernstein, M., Berg, A., Li, F.F. features on key frames. Second, since Light Flow is very small and has comparable computation with the detection network Ndet, sparse feature propagation is applied on the intermediate feature maps of the detection network (see Section 3.3, the 256-d feature maps in RPN [5], and the 490-d feature maps in Light-Head R-CNN [23]), to further reduce computations for non-key frame. Moreover, the incidents are detected very fast. 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. Learning Region Features for Object Detection Jiayuan Gu*, Han Hu, Liwei Wang, Yichen Wei, and Jifeng Dai European Conference on Computer Vision (ECCV), 2018. 1, . Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Third, we apply GRU only on sparse key frames (e.g., every 10th) instead of consecutive frames. By default, α and β are set as 1.0. Inference pipeline is illustrated in Figure 2. Following the practice in MobileNet [13], two width multipliers, α and β, are introduced for controlling the computational complexity, by adjusting the network width. It is one order faster than the best previous effort on fast object detection, with on par accuracy (see Figure 1). The object in each image is very small, approximately 55 by 15. R-fcn: Object detection via region-based fully convolutional A more cheaper Nflow is so necessary. very small network, Light Flow, is designed for establishing correspondence We tried training on sequences of 2, 4, 8, 16, and 32 frames. It only causes minor drop in accuracy (15% increasing in end-point error) but significantly speeds up by nearly 65× theoretically (see Table. %PDF-1.5 We do not dive into the details of varying technical designs. TensorFlow: Large-scale machine learning on heterogeneous systems 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. 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. 30 object categories are involved, which are a subset of ImageNet DET annotated categories. Theoretical computation is counted in FLOPs (floating point operations, note that a multiply-add is counted as 2 operations). In SGD, 240k iterations are performed on 4 GPUs, with each GPU holding one mini-batch. On all frames, we present Light Flow, a very small deep neural network to estimate feature flow, which offers instant availability on mobiles. Inference on the untrimmed video sequences leads to accuracy on par with that of trimmed, and can be implemented easier. 16 Apr 2018 • Xizhou Zhu • Jifeng Dai • Xingchi Zhu • Yichen Wei • Lu Yuan. A 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]. The curve is drawn by adjusting the key frame duration l. We can see that the curve with flow guidance surpasses that without flow guidance. On top of it, our system can further significantly improve the speed-accuracy trade-off curve. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic share, Despite the recent success of video object detection on Desktop GPUs, its Discrimination, Object detection at 200 Frames Per Second. ∙ Light weight image object detector is applied on sparse key frames. Following the protocol in [32], the accuracy is evaluated by the average end-point error (EPE). 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}. Such a way can reduce end-point error by nearly 10%. Bibliographic details on Towards High Performance Video Object Detection for Mobiles. Instead, there are very limited computational capability and runtime memory on mobiles. However, [20] aggregates feature maps from nearby frame in a linear and memoryless way. Though recursive aggregation [21]. With the increasing interests in computer vision use cases like self-driving cars, face recognition, intelligent transportation systems and etc. Nevertheless, video object detection has received little attention, although it is more challenging and more important in practical scenarios. Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. Rectifier nonlinearities improve neural network acoustic models. In this paper, we The procedure consists of a matching stage for finding correspondences between reference and output objects, an accuracy score that is sensitive to object shapes as well as boundary and fragmentation errors, and a ranking step for final ordering of the algorithms using multiple performance indicators. One of the most popular datasets used in academia is ImageNet, composed of millions of classified images, (partially) utilized in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) annual competition. 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. Compared with the original FlowNet design in [32], Light Flow (β=1.0) can achieve 65.2× theoretical speedup with 14.9× less parameters. It is also unclear whether the ∙ Cited by: 65 | Bibtex | Views 89 | Links. During inference, feature maps on any non-key frame i are propagated from its preceding key frame k by. Authors: Xizhou Zhu, Jifeng Dai, Xingchi Zhu, Yichen Wei, Lu Yuan (Submitted on 16 Apr 2018) Abstract: Despite the recent success of video object detection on Desktop GPUs, its architecture is still far too heavy for mobiles. Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., This is translated into a low Mean Time to Detect (MTTD) and a low False Alarm Rate (FAR). Lightweight image object detector is an indispensable component for our video object detection system. Full Text. mobiles. Despite the recent success of video object detection on Desktop GPUs, its architecture is still far too heavy for mobiles. Comprehensive experiments show that the model steadily pushes forward the performance (speed-accuracy trade-off) envelope, towards high performance video object detection on mobiles. The detection system utilizing Light Flow achieves accuracy very close to that utilizing the heavy-weight FlowNet (61.2% v.s.

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