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Pedestrian attribute inference is a demanding problem in visual surveillancethat can facilitate person retrieval, search and indexing. To exploit semanticrelations between attributes, recent research treats it as a multi-label imageclassification task. The visual cues hinting at attributes can be stronglylocalized and inference of person attributes such as hair, backpack, shorts,etc., are highly dependent on the acquired view of the pedestrian. In thispaper we assert this dependence in an end-to-end learning framework and showthat a view-sensitive attribute inference is able to learn better attributepredictions. Our proposed model jointly predicts the coarse pose (view) of thepedestrian and learns specialized view-specific multi-label attributepredictions. We show in an extensive evaluation on three challenging datasets(PETA, RAP and WIDER) that our proposed end-to-end view-aware attributeprediction model provides competitive performance and improves on the publishedstate-of-the-art on these datasets.

Deep View-Sensitive Pedestrian Attribute Inference in an end-to-end Model