Data Efficient Approaches on Deep Action Recognition in Videos
Sathya R1, Rugveda Muralidhar I2, Sai Harsha Vardhan K3, Sri Karan R4, Arun Reddy B5
1Mrs. Sathya R, Assistant Professor, Department of Computer Science and Engineering, SRM IST, Chennai (Tamil Nadu), India.
2Rugveda Muralidhar I, Department of Computer Science and Engineering, SRM IST, Chennai (Tamil Nadu), India.
3Sai Harsha Vardhan K, Department of Computer Science and Engineering, SRM IST, Chennai (Tamil Nadu), India.
4Sri Karan R, Department of Computer Science and Engineering, SRM IST, Chennai (Tamil Nadu), India.
5Arun Reddy B, Department of Computer Science and Engineering, SRM IST, Chennai (Tamil Nadu), India.
Manuscript received on 18 April 2019 | Revised Manuscript received on 25 April 2019 | Manuscript published on 30 April 2019 | PP: 385-391 | Volume-8 Issue-4, April 2019 | Retrieval Number: D6160048419/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this paper, we ipropose an efficient visual tracker, which specifically catches a ibounding box containing the target object in a video by methods for successive iactivities got the hang of utilizing deep neural networks. The iproposed deep neural network to control following activities is ipre-prepared utilizing different preparing video sequences and calibrated amid igenuine following for online adjustment to a difference in target and background. The pre-training is done by using deep Reinforcement ilearning just as directed learning. The utilization of RL iempowers even mostly named data to be effectively used for semi-directed learning. Through the assessment of the item following ibenchmark data set, the proposed tracker is approved to accomplish an aggressive exhibition at three times the speed of present deep network-based trackers.
Keywords: CNN, DAP3D-Net, Computer Vision, Pre-Training
Scope of the Article: Pattern Recognition