2019-10-15-zhuang19a.md

title	crossref	abstract	layout	series	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	publisher	container-title	volume	genre	issued	pdf	extras
Multi-branch Siamese Network for High Performance Online Visual Tracking	acml19	Recently, Siamese networks have drawn great attention in the visual tracking community because of their balanced accuracy and speed. However, most existing Siamese frameworks describe the target appearance using a global pattern from the last layer, leading to high sensitivity to similar distractors, non-rigid appearance change, and partial occlusion. Addressing these issues, we propose a Multi-branch Siamese network (MSiam) for high-performance object tracking. The MSiam performs layer-wise feature aggregations and simultaneously considers the global-local patterns for more accurate target tracking. In particular, we propose a feature aggregation module (FAM) keeping the heterogeneity of the three types of features, further improving the discriminability of MSiam using both high-level semantic and low-level spatial information. To enhance the adaptability to non-rigid appearance change and partial occlusion, a multi-scale local pattern detection module (LPDM) is designed to identify discriminative regions of the target objects. By considering various combinations of the local structures, our tracker can form various types of structure patterns. Extensive evaluations on five benchmarks demonstrate that the proposed tracking algorithm performs favorably against state-of-the-art methods while running beyond real-time.	inproceedings	Proceedings of Machine Learning Research	zhuang19a	0	Multi-branch Siamese Network for High Performance Online Visual Tracking	519	534	519-534	519	false	Zhuang, Junfei and Dong, Yuan and Bai, Hongliang and Wang, Gang	given family Junfei Zhuang given family Yuan Dong given family Hongliang Bai given family Gang Wang	2019-10-15		PMLR	Proceedings of The Eleventh Asian Conference on Machine Learning	101	inproceedings	date-parts 2019 10 15	http://proceedings.mlr.press/v101/zhuang19a/zhuang19a.pdf