Multi-object Tracking Method Based on Efficient Channel Attention and Switchable Atrous Convolution

Improved object tracking method based on mean shift and particle filter

Journal of Computer Applications ◽

10.3724/sp.j.1087.2012.00504 ◽

2013 ◽

Vol 32 (2) ◽

pp. 504-506

Author(s):

Ke LI ◽

Ke-hu XU ◽

Da-shan HUANG

Keyword(s):

Particle Filter ◽

Object Tracking ◽

Mean Shift ◽

Tracking Method

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Research of single object tracking method based on Siamese Network and Level Set

2020 The 4th International Conference on Video and Image Processing ◽

10.1145/3447450.3447477 ◽

2020 ◽

Author(s):

Tianbo Liu ◽

Li Su ◽

Shuai Yuan ◽

Gong Cheng ◽

Feng Zhang

Keyword(s):

Object Tracking ◽

Level Set ◽

Single Object ◽

Tracking Method ◽

Siamese Network

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Multi-Scale Anti-Occlusion Correlation Filters Object Tracking Method Based on Complementary Features

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001421550028 ◽

2020 ◽

pp. 2155002

Author(s):

Xiuhua Hu ◽

Yuan Chen ◽

Yan Hui ◽

Yingyu Liang ◽

Guiping Li ◽

...

Keyword(s):

Object Tracking ◽

Scale Model ◽

Correlation Filters ◽

Motion Feature ◽

Tracking Method ◽

Object State ◽

Multi Scale ◽

Tracking Process ◽

Object Appearance ◽

Response Peak

Aiming to tackle the problem of tracking drift easily caused by complex factors during the tracking process, this paper proposes an improved object tracking method under the framework of kernel correlation filter. To achieve discriminative information that is not sensitive to object appearance change, it combines dimensionality-reduced Histogram of Oriented Gradients features and Lab color features, which can be used to exploit the complementary characteristics robustly. Based on the idea of multi-resolution pyramid theory, a multi-scale model of the object is constructed, and the optimal scale for tracking the object is found according to the confidence maps’ response peaks of different sizes. For the case that tracking failure can easily occur when there exists inappropriate updating in the model, it detects occlusion based on whether the occlusion rate of the response peak corresponding to the best object state is less than a set threshold. At the same time, Kalman filter is used to record the motion feature information of the object before occlusion, and predict the state of the object disturbed by occlusion, which can achieve robust tracking of the object affected by occlusion influence. Experimental results show the effectiveness of the proposed method in handling various internal and external interferences under challenging environments.

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