Video Surveillance for Road Traffic Monitoring
This work addresses traffic monitoring for urban management, but it is incremental as it applies existing techniques to a specific challenge.
The paper tackled vehicle tracking across multiple cameras in urban intersections for the AI-City Challenge, presenting qualitative results using standard metrics like mAP and IDF1.
This paper presents the learned techniques during the Video Analysis Module of the Master in Computer Vision from the Universitat Autònoma 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.