In: Computer Vision and Pattern Recognition. He, S., Yang, Q., Wang, J., Yang, M.H.: Visual tracking via locality sensitive histograms. Lu, Z., Lin, Y.-R., Huang, X., Xiong, N., Fang, Z.: Visual topic discovering, tracking and summarization from social media streams. Wang, Y., et al.: Dynamic propagation characteristics estimation and tracking based on an EM-EKF algorithm in time-variant MIMO channel. Lee, H., Jung, M., Tani, J.: Recognition of visually perceived compositional human actions by multiple spatio-temporal scales recurrent neural networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. Girdhar, R., Ramanan, D., Gupta, A., Sivic, J., Russell, B.: ActionVLAD: learning spatio-temporal aggregation for action classification. In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), Santa Rosa, CA, pp. IEEE Access 5, 12856–12864 (2017)īaek, S., Kim, K.I., Kim, T.: Real-time online action detection forests using spatio-temporal contexts. Zhou, X., Liu, X., Yang, C., Jiang, A., Yan, B.: Multi-channel features spatio-temporal context learning for visual tracking. Zhang, D., Maei, H., Wang, X., Wang, Y.F.: Deep Reinforcement Learning for Visual Object Tracking in Videos, p. Lu, X., Chen, S., Xiong, N.: ViMediaNet: an emulation system for interactive multimedia based telepresence services. įang, W., Li, Y., Zhang, H., Xiong, N., Lai, J., Vasilakos, A.V.: On the through put-energy trade off for data transmission between cloud and mobile devices. īertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., Torr, P.H.S.: Fully-convolutional siamese networks for object tracking. In: Leibe, B., Matas, J., Sebe, N., Welling, M. Liu, W., et al.: SSD: single shot MultiBox detector. Gao, L., Yu, F., Chen, Q., Xiong, N.: Consistency maintenance of do and undo/redo operations in real-time collaborative bitmap editing systems. Xia, Z., Wang, X., Sun, X., Liu, Q., Xiong, N.: Steganalysis of LSB matching using differences between nonadjacent pixels. Gui, J., Hui, L., Xiong, N.X.: A game-based localized multi-objective topology control scheme in heterogeneous wireless networks. Girshick, R., Donahue, J., Darrell, T.: Region-based convolutional networks for accurate object detection and segmentation. In: IEEE Conference on Computer Vision & Pattern Recognition, pp. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. Nam, H., Han, B.: Learning multi-domain convolutional neural networks for visual tracking. Wu, P.F., Xiao, F., Sha, C., Huang, H.P., Wang, R.C., Xiong, N.: Node scheduling strategies for achieving full-view area coverage in camera sensor networks. In: IEEE International Conference on Computer Vision, pp. Wang, L., Ouyang, W., Wang, X.: Visual tracking with fully convolutional networks. Wang, N., Yeung, D.Y.: Learning a deep compact image representation for visual tracking. Wu, Y., Lim, J., Yang, M.H.: Online object tracking: a benchmark. The tracking model proposed above demonstrates good performance in an existing tracking benchmark. Specifically, Spatial-Temporal Context learning (STC) algorithm is added into our model to achieve its tracking performance more efficiently. In order to maximize tracking performance and make a great use the continuous, inter-frame correlation in the long term, this paper harnesses the power of deep reinforcement learning (RL) algorithm. So a recurrent convolutional neural network is adopted acting as an agent in this model, with the important insight that it can interact with the video overtime. Considering the tracking task can be processed as a sequential decision-making process and historical semantic coding that is highly relevant to future decision-making information. Crucially, this task is tackled in an end-to-end approach. This paper presents a novel model for IoT video sensors object tracking via deep Reinforcement Learning (RL) algorithm and spatial-temporal context learning algorithm, which provides a tracking solution to directly predict the bounding box locations of the target at every successive frame in video surveillance. The new video sensor network has gradually become a research hotspot in the field of wireless sensor network, and its rich perceptual information is more conducive to the realization of target positioning and tracking function. Using the IoT, different items or devices can be allowed to continuously generate, obtain, and exchange information. The Internet of Things (IoT) is the upcoming one of the major networking technologies.
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