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Person re identification is a challenging retrieval task that requiresmatching a person's acquired image across non overlapping camera views. In thispaper we propose an effective approach that incorporates both the fine andcoarse pose information of the person to learn a discriminative embedding. Incontrast to the recent direction of explicitly modeling body parts orcorrecting for misalignment based on these, we show that a ratherstraightforward inclusion of acquired camera view and/or the detected jointlocations into a convolutional neural network helps to learn a very effectiverepresentation. To increase retrieval performance, re-ranking techniques basedon computed distances have recently gained much attention. We propose a newunsupervised and automatic re-ranking framework that achieves state-of-the-artre-ranking performance. We show that in contrast to the currentstate-of-the-art re-ranking methods our approach does not require to computenew rank lists for each image pair (e.g., based on reciprocal neighbors) andperforms well by using simple direct rank list based comparison or even by justusing the already computed euclidean distances between the images. We show thatboth our learned representation and our re-ranking method achievestate-of-the-art performance on a number of challenging surveillance image andvideo datasets.  The code is available online at:https://github.com/pse-ecn/pose-sensitive-embedding

A Pose-Sensitive Embedding for Person Re-Identification with Expanded Cross Neighborhood Re-Ranking