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DeepTrackNet: Camera Based End to End Deep Learning Framework for Real Time Detection, Localization and Tracking for Autonomous Vehicles

Publication Type : Conference Paper

Publisher : Advances in Intelligent Systems and Computing

Source : Advances in Intelligent Systems and Computing (2020)

ISBN : 9783030304645

Campus : Coimbatore

School : School of Engineering

Center : Computational Engineering and Networking

Department : Electronics and Communication

Year : 2020

Abstract : Vehicle detection and tracking in real time remains as the most challenging tasks for the autonomous vehicles and mobile robots. The designed algorithms should have less latency and with high accuracy and performance. This paper proposed a unified end to end deep learning framework for real-time detection, localization and tracking for autonomous vehicles called DeepTrackNet. In the proposed DeepTrackNet architecture Mobilenet SSD is used for vehicle detection, localization and deep regression network is used for vehicle tracking. Based on the experimental analysis performed we infer that the proposed DeepTrackNet architecture has produced satisfactory results for real-time vehicle detection and tracking on an Nvidia Jetson embedded computing platform using monocular camera with a reasonable latency. Mobilenet SSD took 84 (ms) median detection time and deep regression tracker has the least overall average failure frames at 4.388 on the VTB TB-100 dataset (http://cvlab.hanyang.ac.kr/tracker_benchmark/datasets.html(link is external)). The code along with detailed results are available in the GitHub repository (https://github.com/dineshresearch/DeepTrackNet(link is external)).

Cite this Research Publication : D. Amara, Karthika, R., and Dr. Soman K. P., “DeepTrackNet: Camera Based End to End Deep Learning Framework for Real Time Detection, Localization and Tracking for Autonomous Vehicles”, in Advances in Intelligent Systems and Computing, 2020.

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