کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
378337 1437212 2016 16 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Salient pairwise spatio-temporal interest points for real-time activity recognition
ترجمه فارسی عنوان
نقاط مورد علاقه برجسته دو به دو فضا ـ زمانی برای تشخیص فعالیت در زمان واقعی
کلمات کلیدی
نقطه بهره فضا زمانی؛ کیسه بصری کلمات؛ رخداد
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

Real-time Human action classification in complex scenes has applications in various domains such as visual surveillance, video retrieval and human robot interaction. While, the task is challenging due to computation efficiency, cluttered backgrounds and intro-variability among same type of actions. Spatio-temporal interest point (STIP) based methods have shown promising results to tackle human action classification in complex scenes efficiently. However, the state-of-the-art works typically utilize bag-of-visual words (BoVW) model which only focuses on the word distribution of STIPs and ignore the distinctive character of word structure. In this paper, the distribution of STIPs is organized into a salient directed graph, which reflects salient motions and can be divided into a time salient directed graph and a space salient directed graph, aiming at adding spatio-temporal discriminant to BoVW. Generally speaking, both salient directed graphs are constructed by labeled STIPs in pairs. In detail, the “directional co-occurrence” property of different labeled pairwise STIPs in same frame is utilized to represent the time saliency, and the space saliency is reflected by the “geometric relationships” between same labeled pairwise STIPs across different frames. Then, new statistical features namely the Time Salient Pairwise feature (TSP) and the Space Salient Pairwise feature (SSP) are designed to describe two salient directed graphs, respectively. Experiments are carried out with a homogeneous kernel SVM classifier, on four challenging datasets KTH, ADL and UT-Interaction. Final results confirm the complementary of TSP and SSP, and our multi-cue representation TSP + SSP + BoVW can properly describe human actions with large intro-variability in real-time.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: CAAI Transactions on Intelligence Technology - Volume 1, Issue 1, January 2016, Pages 14–29
نویسندگان
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