multi_object_tracking
FastMOT
| multi_object_tracking | FastMOT | |
|---|---|---|
| 1 | 2 | |
| 1 | 1,095 | |
| - | - | |
| 2.7 | 0.0 | |
| about 3 years ago | over 2 years ago | |
| Python | Python | |
| GNU Affero General Public License v3.0 | MIT License |
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multi_object_tracking
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Show HN: Critter.Camera – Browser based motion detection and image capture
I also tackled the problem of moving-window brightness calibration in a related (but more constrained) problem --- tracking water fleas moving in a tank of water where an overhead light might periodically turn on and wash out the picture.
I essentially subtracted the current frame from the max across a moving window.
I'd be interested to chat about it if you like, my email is in my profile. The code is at https://github.com/nuchi/multi_object_tracking
FastMOT
- Does Multi Object Tracking work better (precision/recall) on videos than jury rigging a SOTA image object detection to work on videos?
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Assign ID and track moving object with optical flow
On failure, you can try using a re-identification methods like FastReid: https://github.com/JDAI-CV/fast-reid in combination with your detector. A good pipeline that combines everything you seem to need is here: https://github.com/GeekAlexis/FastMOT. It uses a combination of Yolov4 (detector) + Kalman filters, Optical flow (tracker) and FastReid (re-identification)
What are some alternatives?
ByteTrack - [ECCV 2022] ByteTrack: Multi-Object Tracking by Associating Every Detection Box
multi-object-tracker - Multi-object trackers in Python
FairMOT - [IJCV-2021] FairMOT: On the Fairness of Detection and Re-Identification in Multi-Object Tracking
Deep-SORT-YOLOv4 - People detection and optional tracking with Tensorflow backend.
fast-reid - SOTA Re-identification Methods and Toolbox
TFJS-object-detection - Real-time custom object detection in the browser using tensorflow.js
zero-shot-object-tracking - Object tracking implemented with the Roboflow Inference API, DeepSort, and OpenAI CLIP.
mmtracking - OpenMMLab Video Perception Toolbox. It supports Video Object Detection (VID), Multiple Object Tracking (MOT), Single Object Tracking (SOT), Video Instance Segmentation (VIS) with a unified framework.
yolov4-custom-functions - A Wide Range of Custom Functions for YOLOv4, YOLOv4-tiny, YOLOv3, and YOLOv3-tiny Implemented in TensorFlow, TFLite, and TensorRT.
AI-basketball-analysis - :basketball::robot::basketball: AI web app and API to analyze basketball shots and shooting pose.