Video object tracking based on YOLOv7 and DeepSORT

TL;DR

This paper introduces YOLOv7-DeepSORT, a multiple object tracking method that combines the improved YOLOv7 detection network with DeepSORT, demonstrating enhanced tracking accuracy over the previous YOLOv5-DeepSORT approach.

Contribution

The paper proposes integrating YOLOv7 with DeepSORT for MOT, improving detection performance and tracking accuracy compared to the YOLOv5-based method.

Findings

YOLOv7-DeepSORT outperforms YOLOv5-DeepSORT in tracking accuracy.

The method is effective for applications like autonomous driving and surveillance.

Experimental results validate the improved performance of the proposed approach.

Abstract

Multiple object tracking (MOT) is an important technology in the field of computer vision, which is widely used in automatic driving, intelligent monitoring, behavior recognition and other directions. Among the current popular MOT methods based on deep learning, Detection Based Tracking (DBT) is the most widely used in industry, and the performance of them depend on their object detection network. At present, the DBT algorithm with good performance and the most widely used is YOLOv5-DeepSORT. Inspired by YOLOv5-DeepSORT, with the proposal of YOLOv7 network, which performs better in object detection, we apply YOLOv7 as the object detection part to the DeepSORT, and propose YOLOv7-DeepSORT. After experimental evaluation, compared with the previous YOLOv5-DeepSORT, YOLOv7-DeepSORT performances better in tracking accuracy.