A Robust Deep Networks based Multi-Object MultiCamera Tracking System
  for City Scale Traffic
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.
6814701ddf384f8f283f91b6
Muhammad Imran Zaman
claimed_verified
2025-05-04T10:05:05.552Z
659d5355d09adee457236d53
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/y1W6Co4jIYB95Cx6Tjrsd.jpeg
Muhammad Imran Zaman
user
ImranzamanML
6814701ddf384f8f283f91b7
Usama Ijaz Bajwa
6814701ddf384f8f283f91b8
Gulshan Saleem
6814701ddf384f8f283f91b9
Rana Hammad Raza
A Robust Deep Networks based Multi-Object MultiCamera Tracking System
  for City Scale Traffic
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.
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.
Mask R-CNN Non-Maximum Suppression (NMS) transfer learning ResNet-152 Deep SORT IDF1 score precision recall