nwojke/deep_sort
Deep Sort
DeepSORT is a real-time multi-object tracking framework designed to maintain consistent identities of multiple objects across video frames. It integrates deep learning appearance features with motion descriptors to track objects through a sequence of video data.
The system uses a deep convolutional neural network to generate high-dimensional visual descriptors for person re-identification. These appearance features are combined with motion estimation via Kalman filtering and solved using the Hungarian algorithm to optimally associate detections with existing tracks.
The framework includes capabilities for gating-based association filtering and state-based track management to handle object lifecycles. It also provides tools for rendering tracking results onto video frames and evaluating tracking performance against established benchmarks.
Features
- Object Tracking Frameworks - Provides a real-time multi-object tracking framework combining motion descriptors and deep appearance features.
- Feature Descriptor Computation - Generates numerical feature descriptors for bounding boxes to enable similarity comparison.
- Object Tracking Systems - Maintains consistent identities of multiple objects across a sequence of video frames.
- Detection-to-Track Association - Uses the Hungarian algorithm to optimally pair detections with tracks via a cost matrix.
- Appearance Matching Networks - Implements appearance matching networks to associate detected objects with existing tracks using visual embeddings.
- Appearance Embedding Extraction - Extracts appearance embeddings from bounding boxes for person re-identification.
- Kalman Filter Trackers - Employs Kalman filters to predict future object positions based on velocity and bounding box coordinates.
- Video Object Tracking - Integrates appearance and motion data to track multiple objects across video frames in real time.
- Person Re-identification - Implements person re-identification to recognize individuals across different camera views or frames.
- CNN Feature Extractors - Uses a deep convolutional neural network to generate high-dimensional visual descriptors for object identification.
- Association Gating Filters - Restricts the matching search space by ignoring candidates outside a maximum distance threshold.
- Deep Learning Association Metrics - Associates bounding boxes across video frames using deep feature vectors and cosine similarity.
- Identity Association Metrics - Determines if bounding boxes in different frames represent the same object using deep learning feature vectors.
- Track Lifecycle Management - Maintains track lifecycles by counting consecutive frames of non-detection before deleting a track.
- Real-Time Video Analytics - Processes live video streams to identify and follow specific objects in real time.
Star history
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