کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4946086 1439270 2017 57 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
People detection and articulated pose estimation framework for crowded scenes
ترجمه فارسی عنوان
تشخیص افراد و ایجاد چارچوب تخمینی برای صحنه های شلوغ
کلمات کلیدی
صحنه های مردمی مدل سلسله مراتبی، مدل مشترک، ماشین آلات بردار پشتیبانی، مدل های جزئی قابل تغییر، درصد قطعات به درستی متصل،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
In this paper, we propose a novel articulated pose estimation framework for the simultaneous detection of the human as a whole and their constituent body parts in crowded scenes. The model uses a single discriminative classifier that searches for dependent limbs thereby alleviating the independent inference limitation of other state-of-the-art models. The proposed framework is a hierarchical model that detects humans at both macro and micro levels by fusing global and local detectors. The proposed methodology is validated using a publicly available crowd dataset captured indoors in a sports stadium. Detection results are assessed using the percentage of correctly localized parts (PCP) evaluation metric and compared against competing baselines. Our experimental results report mean detection accuracy of 85% for the global upper body, 95% for the head, 82% for the torso, 71% and 60% for upper and lower arms respectively. A systematic analysis of results also verifies that the proposed model outperforms the state-of-the-art models in terms of detection rate, accuracy and computational complexity.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Knowledge-Based Systems - Volume 131, 1 September 2017, Pages 83-104
نویسندگان
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