title: A Robust Deep Networks based Multi-Object MultiCamera Tracking System
    for City Scale Traffic
summary: Vision sensors are becoming more important in Intelligent Transportation
  Systems (ITS) for traffic monitoring, management, and optimization as the
  number of network cameras continues to rise. However, manual object tracking
  and matching across multiple non-overlapping cameras pose significant
  challenges in city-scale urban traffic scenarios. These challenges include
  handling diverse vehicle attributes, occlusions, illumination variations,
  shadows, and varying video resolutions. To address these issues, we propose an
  efficient and cost-effective deep learning-based framework for Multi-Object
  Multi-Camera Tracking (MO-MCT). The proposed framework utilizes Mask R-CNN for
  object detection and employs Non-Maximum Suppression (NMS) to select target
  objects from overlapping detections. Transfer learning is employed for
  re-identification, enabling the association and generation of vehicle tracklets
  across multiple cameras. Moreover, we leverage appropriate loss functions and
  distance measures to handle occlusion, illumination, and shadow challenges. The
  final solution identification module performs feature extraction using
  ResNet-152 coupled with Deep SORT based vehicle tracking. The proposed
  framework is evaluated on the 5th AI City Challenge dataset (Track 3),
  comprising 46 camera feeds. Among these 46 camera streams, 40 are used for
  model training and validation, while the remaining six are utilized for model
  testing. The proposed framework achieves competitive performance with an IDF1
  score of 0.8289, and precision and recall scores of 0.9026 and 0.8527
  respectively, demonstrating its effectiveness in robust and accurate vehicle
  tracking.
paper_authors._id: 6814701ddf384f8f283f91b6
paper_authors.name: Muhammad Imran Zaman
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-04T10:05:05.552Z
paper_authors._id: 659d5355d09adee457236d53
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/y1W6Co4jIYB95Cx6Tjrsd.jpeg
paper_authors.fullname: Muhammad Imran Zaman
paper_authors.type: user
paper_authors.user: ImranzamanML
paper_authors._id: 6814701ddf384f8f283f91b7
paper_authors.name: Usama Ijaz Bajwa
paper_authors._id: 6814701ddf384f8f283f91b8
paper_authors.name: Gulshan Saleem
paper_authors._id: 6814701ddf384f8f283f91b9
paper_authors.name: Rana Hammad Raza
paper_title: A Robust Deep Networks based Multi-Object MultiCamera Tracking System
    for City Scale Traffic
paper_summary: Vision sensors are becoming more important in Intelligent Transportation
  Systems (ITS) for traffic monitoring, management, and optimization as the
  number of network cameras continues to rise. However, manual object tracking
  and matching across multiple non-overlapping cameras pose significant
  challenges in city-scale urban traffic scenarios. These challenges include
  handling diverse vehicle attributes, occlusions, illumination variations,
  shadows, and varying video resolutions. To address these issues, we propose an
  efficient and cost-effective deep learning-based framework for Multi-Object
  Multi-Camera Tracking (MO-MCT). The proposed framework utilizes Mask R-CNN for
  object detection and employs Non-Maximum Suppression (NMS) to select target
  objects from overlapping detections. Transfer learning is employed for
  re-identification, enabling the association and generation of vehicle tracklets
  across multiple cameras. Moreover, we leverage appropriate loss functions and
  distance measures to handle occlusion, illumination, and shadow challenges. The
  final solution identification module performs feature extraction using
  ResNet-152 coupled with Deep SORT based vehicle tracking. The proposed
  framework is evaluated on the 5th AI City Challenge dataset (Track 3),
  comprising 46 camera feeds. Among these 46 camera streams, 40 are used for
  model training and validation, while the remaining six are utilized for model
  testing. The proposed framework achieves competitive performance with an IDF1
  score of 0.8289, and precision and recall scores of 0.9026 and 0.8527
  respectively, demonstrating its effectiveness in robust and accurate vehicle
  tracking.
paper_ai_summary: A deep learning framework using Mask R-CNN, transfer learning, ResNet-152, and Deep SORT achieves competitive performance in multi-object, multi-camera vehicle tracking under diverse urban traffic conditions.
paper_ai_keywords: Mask R-CNN Non-Maximum Suppression (NMS) transfer learning ResNet-152 Deep SORT IDF1 score precision recall