TrackNet: Simultaneous Object Detection and Tracking and Its Application in Traffic Video Analysis

TL;DR

TrackNet is a novel deep learning framework that simultaneously detects and tracks objects in videos by predicting 3D bounding tubes, streamlining traffic video analysis.

Contribution

The paper introduces TrackNet, a unified network that performs detection and tracking in a single step using a Tube Proposal Network within a Faster R-CNN framework.

Findings

Achieved promising results on UA-DETRAC traffic dataset.

Successfully detects and tracks multiple vehicles in traffic videos.

Outperforms traditional tracking-by-detection methods.

Abstract

Object detection and object tracking are usually treated as two separate processes. Significant progress has been made for object detection in 2D images using deep learning networks. The usual tracking-by-detection pipeline for object tracking requires that the object is successfully detected in the first frame and all subsequent frames, and tracking is done by associating detection results. Performing object detection and object tracking through a single network remains a challenging open question. We propose a novel network structure named trackNet that can directly detect a 3D tube enclosing a moving object in a video segment by extending the faster R-CNN framework. A Tube Proposal Network (TPN) inside the trackNet is proposed to predict the objectness of each candidate tube and location parameters specifying the bounding tube. The proposed framework is applicable for detecting and tracking any object and in this paper, we focus on its application for traffic video analysis. The proposed model is trained and tested on UA-DETRAC, a large traffic video dataset available for multi-vehicle detection and tracking, and obtained very promising results.