18 July 2017 Semantic attributes for people’s appearance description: an appearance modality for video surveillance applications
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Abstract
Using semantic attributes such as gender, clothes, and accessories to describe people’s appearance is an appealing modeling method for video surveillance applications. We proposed a midlevel appearance signature based on extracting a list of nameable semantic attributes describing the body in uncontrolled acquisition conditions. Conventional approaches extract the same set of low-level features to learn the semantic classifiers uniformly. Their critical limitation is the inability to capture the dominant visual characteristics for each trait separately. The proposed approach consists of extracting low-level features in an attribute-adaptive way by automatically selecting the most relevant features for each attribute separately. Furthermore, relying on a small training-dataset would easily lead to poor performance due to the large intraclass and interclass variations. We annotated large scale people images collected from different person reidentification benchmarks covering a large attribute sample and reflecting the challenges of uncontrolled acquisition conditions. These annotations were gathered into an appearance semantic attribute dataset that contains 3590 images annotated with 14 attributes. Various experiments prove that carefully designed features for learning the visual characteristics for an attribute provide an improvement of the correct classification accuracy and a reduction of both spatial and temporal complexities against state-of-the-art approaches.
© 2017 SPIE and IS&T
Mayssa Frikha, Emna Fendri, Mohamed Hammami, "Semantic attributes for people’s appearance description: an appearance modality for video surveillance applications," Journal of Electronic Imaging 26(5), 051405 (18 July 2017). https://doi.org/10.1117/1.JEI.26.5.051405 . Submission: Received: 4 January 2017; Accepted: 22 June 2017
Received: 4 January 2017; Accepted: 22 June 2017; Published: 18 July 2017
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