Video Surveillance for Road Traffic Monitoring

TL;DR

This paper discusses techniques developed for multi-camera vehicle tracking in city traffic surveillance, emphasizing multi-tracking methods and evaluation metrics, with results from the AI-City Challenge.

Contribution

It introduces a multi-tracking methodology for city-wide vehicle monitoring and extends single-camera tracking techniques to multi-camera scenarios.

Findings

Achieved competitive mAP for object detection.

Attained high IDF1 scores for tracking accuracy.

Demonstrated effective multi-camera tracking extension.

Abstract

This paper presents the learned techniques during the Video Analysis Module of the Master in Computer Vision from the Universitat Aut\`onoma de Barcelona, used to solve the third track of the AI-City Challenge. This challenge aims to track vehicles across multiple cameras placed in multiple intersections spread out over a city. The methodology followed focuses first in solving multi-tracking in a single camera and then extending it to multiple cameras. The qualitative results of the implemented techniques are presented using standard metrics for video analysis such as mAP for object detection and IDF1 for tracking. The source code is publicly available at: https://github.com/mcv-m6-video/mcv-m6-2021-team4.