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Person re-identification (re-id) is the task of identifying a person across non-overlapping cameras. Most of the current techniques apply deep learning and achieve a significant accuracy. However, learning a deep model that can generalize well against the challenges of pose variation, occlusion, illumination changes, and low resolution is a difficult task. Toward this, we propose a deep reconstruction re-id network, comprising of an encoder and a multi-resolution decoder, which can learn embeddings invariant to pose, occlusion, illumination, and low resolution. In our model, the encoder acts as a conventional deep re-id network and outputs a discriminative feature embedding. The output feature is then used as an input to the multi-resolution decoder to reconstruct the input images of the same identity under different resolutions, such that they are similar in pose and illumination as well as free from occlusion. We further propose a hybrid sampling strategy to boost the effectiveness of the training loss function. In addition, we propose test set augmentation using the reconstructed images to explicitly transform single query to multi-query setting. In our multi-tasking approach, the feature robustness is enhanced by the multi-resolution decoder, and the overall accuracy is further improved by a sampling strategy and test data augmentation. Furthermore, we empirically show that the proposed network is robust to pose variations, occlusion, and low resolution. We perform rigorous qualitative and quantitative analysis in order to demonstrate that we achieve state-of-the-art person re-id accuracy.

Hdrnet: Person Re-Identification Using Hybrid Sampling in Deep Reconstruction Network