Real-Time Adaptive Object Detection and Tracking for Autonomous Vehicles

Abstract

The recent development of deep neural networks attempts to go deeper through the layered architecture to solve complex problems. As expected, the deepening impacts the processing times for inferences in tasks like object classification and localization. This demands solutions for balancing computational resource requirements and extend the versatility of a given model to suit a variety of applications. Often the solutions rely on architectural modifications to ease training times while achieving more accurate results. In this work, we propose a multistage algorithm with an initial stage adopting the YOLOv3 network for object detection and HART approach for object tracking. This is followed by an adaptive post-localization stage shift system taking into consideration the processing times of stage inferences, which are the number of located objects in image sequences. The goal is to change the localization strategy to achieve optimal processing time performance. For evaluation, we present results in terms of precision and processing times in varying traffic conditions. The results demonstrate the effectiveness of the proposed adaptive multistage model in comparison to other real-time state-of-the-art detection strategies, as it achieves a frame rate gain of 49% over YOLOv3 while maintaining competitive task accuracy.