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Multiple Object Tracking Challenge Technical Report for Team MT_IoT

2022-12-07 · Feng Yan, Zhiheng Li, Weixin Luo, Zequn Jie, Fan Liang, Xiaolin Wei, Lin Ma

This is a brief technical report of our proposed method for Multiple-Object Tracking (MOT) Challenge in Complex Environments. In this paper, we treat the MOT task as a two-stage task including human detection and trajectory matching. Specifically, we designed an improved human detector and associated most of detection to guarantee the integrity of the motion trajectory. We also propose a location-wise matching matrix to obtain more accurate trace matching. Without any model merging, our method achieves 66.672 HOTA and 93.971 MOTA on the DanceTrack challenge dataset.

📄 PDF Abstract BibTeX arXiv:2212.03586

Code (1)

BingfengYan/DS_OCSORT 공식 구현 pytorch

Tasks

Human DetectionMulti-Object TrackingObjectObject TrackingWord Sense Disambiguation

Methods 이 논문이 사용한 방법론

Average Pooling 설명 없음
Global Average Pooling Global Average Pooling is a pooling operation designed to replace fully connected layers in classical CNNs. The idea is to generate one feature map for each corresponding…
1x1 Convolution A 1 x 1 Convolution is a convolution with some special properties in that it can be used for dimensionality reduction,…
Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…
Convolution A convolution is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the…
Residual Connection 설명 없음
Batch Normalization 설명 없음
BNB Customer Service Number +1-833-534-1729 설명 없음

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