Large-Scale Pedestrian Retrieval Competition
This addresses the problem of enhancing person retrieval systems for surveillance applications, but it is incremental as it focuses on competition setup rather than novel algorithmic advances.
The paper introduces the Large-Scale Pedestrian Retrieval Competition (LSPRC), which tackles the challenge of person retrieval in surveillance by providing large-scale real-world data and standard evaluation methods, resulting in improved algorithm robustness for tasks like attribute-based and ReID-based retrieval.
The Large-Scale Pedestrian Retrieval Competition (LSPRC) mainly focuses on person retrieval which is an important end application in intelligent vision system of surveillance. Person retrieval aims at searching the interested target with specific visual attributes or images. The low image quality, various camera viewpoints, large pose variations and occlusions in real scenes make it a challenge problem. By providing large-scale surveillance data in real scene and standard evaluation methods that are closer to real application, the competition aims to improve the robust of related algorithms and further meet the complicated situations in real application. LSPRC includes two kinds of tasks, i.e., Attribute based Pedestrian Retrieval (PR-A) and Re-IDentification (ReID) based Pedestrian Retrieval (PR-ID). The normal evaluation index, i.e., mean Average Precision (mAP), is used to measure the performances of the two tasks under various scale, pose and occlusion. While the method of system evaluation is introduced to evaluate the person retrieval system in which the related algorithms of the two tasks are integrated into a large-scale video parsing platform (named ISEE) combing with algorithm of pedestrian detection.