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Detection, Estimation & Tracking Road Objects for Assisting Driving

The new era of mobility is moving towards automation. Detecting, estimating, and tracking objects from moving vehicles using dash-cam images in real-time can provide substantial advantages in supporting drivers’ decision making in advance. In this paper, an advanced deep learning-based object detection, distance estimation, and tracking framework has been proposed for this purpose. RetinaNet algorithm with ResNeXt backbone network has been used to detect five traffic object classes, including cars, cyclists, pedestrians, buses, and motorcycles, with improved accuracy. Additionally, distance estimation algorithm was introduced to increase both reliability and precession of detection. Moreover, an improved Simple Online and Realtime Tracking (SORT) algorithm were sequentially used to estimate traffic parameters such as volume and approach speed of each of these traffic object classes. The algorithm was trained and tested on stock imagery (COCO2017, MOT16, and TDD) of real-world videos taken from urban arterials with multimodal, signalized traffic operations.