Authors:
Chatchanan Varojpipath
and
Krystian Mikolajczyk
Affiliation:
Imperial College London, London, U.K.
Keyword(s):
Person Re-identification, Visual Surveillance, Drone, Unmanned Aerial Vehicle, Biometrics, Image Retrieval.
Abstract:
There has been a growing interest in drone applications and many computer vision tasks were specifically adapted to drone scenarios such as SLAM, object detection, depth estimation, etc. Person re-identification is one of the tasks that can be effectively performed from drones and new datasets specifically geared towards aerial person imagery emerge. In addition to the common problems found in almost every person re-ID dataset, the most significant difference to static CCTV re-ID is the very different human pose across views from the top and similar appearance of different people but also motion blur, light conditions, low resolution and occlusions. To address these problems, we propose to combine a Part-based Convolutional Baseline (PCB), which exploits local features, with an adaptive weight distribution strategy, which assigns different weights to similar and dissimilar samples. The result shows that our method outperforms the state of the arts by a large margin. In addition, we p
ropose a re-ranking method which aggregates Expanded Cross Neighborhood (ECN) distance and Jaccard distance to compute the final ranking. Compared to the existing methods, our re-ranking achieves 3.30% and 3.03% improvement on mAP and rank-1 accuracy, respectively.
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