2021/05/11 by Pol Albacar, Òscar Lorente, Albacar, Pol +5
Computer Science · Engineering · #Advanced Neural Network Applications #Artificial intelligence #Autonomous Vehicle Technology and Safety #Computer Vision and Pattern Recognition (cs.CV) #Computer graphics (images) #Computer science #Computer vision #FOS: Computer and information sciences #Object (grammar) #Object detection #Real-time computing #Segmentation #Single camera #Source code #Track (disk drive) #Tracking (education) #Video Surveillance and Tracking Methods #Video processing #Video tracking #cs.CV
paper · pdf · doi:10.48550/arxiv.2105.04908
published in arXiv (Cornell University) (Cornell University)
arxiv created 2021/05/11 · openalex publication_date 2021/05/11 · arxiv updated 2021/05/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08
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.