УДК: 004.932.72

Robust multi-object tracking using deep learning framework

Полный текст на elibrary.ru

Публикация в Journal of Optical Technology

Ссылка для цитирования:

Sh. Ch. Pang, Anan Du, Zh. Zh. Yu Robust multi-object tracking using deep learning framework [на англ. яз.] // Оптический журнал. 2015. Т. 82. № 8. С. 33–47.

Sh. Ch. Pang, Anan Du, Zh. Zh. Yu Robust multi-object tracking using deep learning framework [in English] // Opticheskii Zhurnal. 2015. V. 82. № 8. P. 33–47.

Ссылка на англоязычную версию:

Sh. Ch. Pang, Anan Du, and Zh. Zh. Yu, "Robust multi-object tracking using deep learning framework," Journal of Optical Technology. 82(8), 516-527 (2015). https://doi.org/10.1364/JOT.82.000516

Аннотация:

Object appearance representation is crucial to any visual tracker. In order to improve the appearance representation, we propose a deep compact and high-level appearance representation applied to a multi-object tracking algorithm, which is called Deep Multi-object Tracking. In this paper, we adopt the deep learning framework to offline obtain generic image features with auxiliary natural images and online fine-tune our Deep Multi-object Tracking system to adapt to appearance changes of the moving objects. Besides, we have fully considered the temporal information denoting the dynamic duration time of each object. Based on the temporal information and particle filter, our Deep Multi-object Tracking algorithm can effectively generate an online learning and updating model to form a discriminative appearance scheme to achieve successful multi-object tracking. Experiments show that the proposed Deep Multi-object Tracking performs well both indoor and outdoor, where the objects undergo large pose, scale, occlusion, and illumination variations in complex scenes including abnormal ones.

Ключевые слова:

многоцелевое отслеживание, глубокое обучение, временная информация, фильтр частиц, дистанционное обучение

Благодарность:

Работа выполнена при финансовой поддержке Фонда специализированных исследований для программ аспирантуры в сфере высшего образования Китая (грант № 20120061110045) и плана научно-технологического развития провинции Цзилинь (грант № 20150204007GX).

Коды OCIS: 100.4996, 100.4999, 110.4155, 150.4065

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