DepthMOT

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Abstract:

Accurately distinguishing each object is a fundamental goal of Multi-object tracking (MOT) algorithms. However, achieving this goal still remains challenging, primarily due to: (i) For crowded scenes with occluded objects, the high overlap of object bounding boxes leads to confusion among closely located objects. Nevertheless, humans naturally perceive the depth of elements in a scene when observing 2D videos. Inspired by this, even though the bounding boxes of objects are close on the camera plane, we can differentiate them in the depth dimension, thereby establishing a 3D perception of the objects. (ii) For videos with rapidly irregular camera motion, abrupt changes in object positions can result in ID switches. However, if the camera pose are known, we can compensate for the errors in linear motion models. In this paper, we propose DepthMOT, which achieves: (i) detecting and estimating scene depth map end-to-end, (ii) compensating the irregular camera motion by camera pose estimation. Extensive experiments demonstrate the superior performance of DepthMOT in VisDrone-MOT and UAVDT datasets.

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Model Introduction

We intergrate part of monodepth2 and FairMOT to estimate the depth of objects and compensate irregular camera motions. Many thanks to their outstanding works! Below are the motivation and paradigm of DepthMOT.

Installation

Please refer to FairMOT to config virtual environment and prepare data.

For VisDrone, UAVDT, KITTI datasets, the data conversion code are available at src/dataset_tools

Model Zoo

Pretrained model

FairMOT pretrain (COCO) + monodepth2 pretrain (KITTI): BaiduYun, code: us93

VisDrone: BaiduYun, code: za3j

Training

All traning scripts are in ./experiments

Training visdrone:

sh experiments/train_visdrone.sh

Training uavdt:

sh experiments/train_uavdt.sh

Training kitti:

sh experiments/train_kitti.sh

Note that if training kitti, it's recommended to modify the input resolution to (1280, 384) in line 32, src/train.py:

dataset = Dataset(opt, dataset_root, trainset_paths, (1280, 384), augment=False, transforms=transforms)

Testing

Similarly to training, for testing, you need to run:

sh experiments/test_{dataset_name}.sh

Performance

Dataset	HOTA	MOTA	IDF1
VisDrone	42.44	37.04	54.02
UAVDT	66.44	62.28	78.13

Results on KITTI is somehow inferior. Better results are obtaining.