We introduce NanoTrack, a novel multi-object tracking (MOT) method that leverages light-weight object detectors to enhance tracking performance in real-time applications where computational resources are scarce. While light-weight detectors are efficient, they often produce an imbalance in detection quality, generating a significant number of low-scoring detections that pose challenges for tracking algorithms. Our approach innovatively utilizes these low-scoring detections for track initialization and maintenance, addressing the shortcomings observed in existing tracking by two-stage tracking methods like ByteTrack, which struggle with the abundance of low-scoring detections. By integrating two new light-weight modules, Refind High Detection (RHD) and Duplicate Track Checking (DTC), NanoTrack effectively incorporates low-scoring detections into the tracking process. Additionally, we enhance the pseudo-depth estimation technique for improved handling in dense target environments, mitigating issues like ID Switching. Our comprehensive experiments demonstrate that NanoTrack surpasses state-of-the-art two-stage TBD methods, including ByteTrack and SparseTrack, on benchmark datasets such as MOT16, MOT17, and MOT20, thereby establishing a new standard for MOT performance using light-weight detectors.
Download pre-trained Detection models and find more models in Model Zoo or in Release Files
Download pre-trained ReID models and find more models in Model Zoo
Track
$ python track.py --demo video --model your_model_path --reid-model your_reid_path --path your_video_path --tracking-method nanotrackEvaluation
$ python val.py --demo image --model your_model_path --reid-model your_reid_path --benchmark MOT16 --tracking-method nanotrack| Model | Backbone | Resolution | COCO mAP | FLOPS | Params | Pre-train weight |
|---|---|---|---|---|---|---|
| NanoDet-m | ShuffleNetV2 1.0x | 320*320 | 20.6 | 0.72G | 0.95M | Download |
| NanoDet-Plus-m-320 (NEW) | ShuffleNetV2 1.0x | 320*320 | 27.0 | 0.9G | 1.17M | Weight | Checkpoint |
| NanoDet-Plus-m-416 (NEW) | ShuffleNetV2 1.0x | 416*416 | 30.4 | 1.52G | 1.17M | Weight | Checkpoint |
| NanoDet-Plus-m-1.5x-320 (NEW) | ShuffleNetV2 1.5x | 320*320 | 29.9 | 1.75G | 2.44M | Weight | Checkpoint |
| NanoDet-Plus-m-1.5x-416 (NEW) | ShuffleNetV2 1.5x | 416*416 | 34.1 | 2.97G | 2.44M | Weight | Checkpoint |
Notice: The difference between Weight and Checkpoint is the weight only provide params in inference time, but the checkpoint contains training time params.
Legacy Model Zoo
| Model | Backbone | Resolution | COCO mAP | FLOPS | Params | Pre-train weight |
|---|---|---|---|---|---|---|
| NanoDet-m-416 | ShuffleNetV2 1.0x | 416*416 | 23.5 | 1.2G | 0.95M | Download |
| NanoDet-m-1.5x | ShuffleNetV2 1.5x | 320*320 | 23.5 | 1.44G | 2.08M | Download |
| NanoDet-m-1.5x-416 | ShuffleNetV2 1.5x | 416*416 | 26.8 | 2.42G | 2.08M | Download |
| NanoDet-m-0.5x | ShuffleNetV2 0.5x | 320*320 | 13.5 | 0.3G | 0.28M | Download |
| NanoDet-t | ShuffleNetV2 1.0x | 320*320 | 21.7 | 0.96G | 1.36M | Download |
| NanoDet-g | Custom CSP Net | 416*416 | 22.9 | 4.2G | 3.81M | Download |
| NanoDet-EfficientLite | EfficientNet-Lite0 | 320*320 | 24.7 | 1.72G | 3.11M | Download |
| NanoDet-EfficientLite | EfficientNet-Lite1 | 416*416 | 30.3 | 4.06G | 4.01M | Download |
| NanoDet-EfficientLite | EfficientNet-Lite2 | 512*512 | 32.6 | 7.12G | 4.71M | Download |
| NanoDet-RepVGG | RepVGG-A0 | 416*416 | 27.8 | 11.3G | 6.75M | Download |