Deep Sort with PyTorch
Overview
Deep Sort with PyTorch is an advanced project implementation of the Multiple Object Tracking (MOT) algorithm, called Deep SORT. It's built to enhance the traditional SORT tracking method by integrating a Convolutional Neural Network (CNN) model that extracts features from images of tracked human entities. This enhancement improves tracking accuracy, particularly in scenarios involving occlusions and complex interactions. The project originally used Faster RCNN for detection but has transitioned to using YOLOv3 and other state-of-the-art models like YOLOv5 and Mask RCNN to generate bounding boxes around objects.
Key Features & Updates
Feature Extraction
Deep Sort with PyTorch now includes multiple updates and features that make the tracking system more robust:
- ResNet Network Integration: Recently, a ResNet network was added for the appearance feature extraction to improve tracking performance.
- Parallel Training Support: Using PyTorch's
nn.parallel.DistributedDataParallel, the feature extraction network can now be trained across multiple GPUs.
Detection
- YOLOv5 Integration: Deep Sort has integrated the YOLOv5 detector, providing alignment with the latest detection technologies. YOLOv5 configuration files have been added, ensuring a streamlined experience.
- Mask RCNN for Segmentation: Added recently, Mask RCNN supports instance segmentation, further enriching the model's capabilities in distinguishing between tracked items.
Tracking
- Category and ID Display: The update allows simultaneous display of an object's category and its tracking ID, improving the user interface and data comprehension.
Setup and Dependencies
To get started with Deep Sort using PyTorch, users need to install several dependencies:
- Python Environment: Ensure Python 3 (possibly Python 2) is installed.
- Essential Libraries: Includes NumPy, SciPy, OpenCV, PyTorch, torchvision, and several others for optimal performance and functionality.
Users can quickly set up their environment by installing the required packages listed in the requirements.txt.
Running the Demo
The project provides several configurations allowing different combinations of object detectors and trackers. With a convenient command-line interface, users can test the following setups, among others:
- YOLOv3 + Deep Sort: For classic detection and sorting.
- YOLOv5s/m + Deep Sort: Utilizing the latest advancements in YOLO versions.
- Mask RCNN + Deep Sort: Incorporating segmentation for detailed tracking.
Practical Applications
- Surveillance Systems: Applying Deep Sort in surveillance can enhance real-time monitoring of public spaces by accurately tracking numerous individuals.
- Intelligent Transportation: It can be utilized to track vehicles and predict traffic patterns.
- Sports Analytics: Provides the ability to track player movements and strategies in detail during games.
Contribution and Further Development
The project is designed to be collaborative, encouraging contributions that refine and extend its capabilities. Efforts such as training detectors on specific datasets or exploring advanced detectors are listed as future directions.
In summary, Deep Sort with PyTorch significantly boosts the potential of real-time object tracking through efficient feature extraction and state-of-the-art detection mechanisms. This project stands out in providing a thorough and adaptable platform for multi-object tracking applications.