Mostexistingpersonre-identification(re-id)methods relyon supervised model learning on per-camera-pair manually labelled pairwise training data. This leads to poor scalability in practical re-id deployment due to the lack of exhaustive identity labelling of image positive and negative pairs for every camera pair. In this work, we address this problem by proposing an unsupervised re-id deep learning approach capable of incrementally discovering and exploiting the underlying re-id discriminative information from automatically generated person tracklet data from videos in an end-to-end model optimisation. We formulate a Tracklet Association Unsupervised Deep Learning (TAUDL) framework characterised by jointly learning per-camera (within-camera) tracklet association (labelling) and cross-camera tracklet correlation by maximising the discovery of most likely tracklet relationships across camera views. Extensive experiments demonstrate the superiority of the proposed TAUDL model over the state-of-the-art unsupervised and domain adaptation re- id methods using six person re-id benchmarking datasets.
In contrast to all these existing unsupervised learning re-id methods, the proposed tracklet association based method enables unsupervised re-id deep end-to-end learning from scratch without any assumption on either the scene characteristic similarity between source and target domains, or the complexity of handling identity label space (or lack of) knowledge transfer in model optimisation. Instead, our method directly learns to discover the re-id discriminative knowledge from unsupervised tracklet label data automatically generated and annotated from the video data using a common deep learning network architecture. Moreover, this method does not assume any overlap of person ID classes across camera views, therefore scalable to any camera networks without any knowledge about camera space-time topology and or time-profiling on people cross-view appearing patterns @cite_44 . Compared to classical unsupervised methods relying on extra hand-crafted features, our method learns tracklet based re-id discriminative features from an end-to-end deep learning process. To our best knowledge, this is the first attempt at unsupervised tracklet association based person re-id deep learning model without relying on any ID labelled training data (either videos or images).
@cite_44
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We propose a novel approach to understanding activities from their partial observations monitored through multiple non-overlapping cameras separated by unknown time gaps. In our approach, each camera view is first decomposed automatically into regions based on the correlation of object dynamics across different spatial locations in all camera views. A new Cross Canonical Correlation Analysis (xCCA) is then formulated to discover and quantify the time delayed correlations of regional activities observed within and across multiple camera views in a single common reference space. We show that learning the time delayed activity correlations offers important contextual information for (i) spatial and temporal topology inference of a camera network; (ii) robust person re-identification and (iii) global activity interpretation and video temporal segmentation. Crucially, in contrast to conventional methods, our approach does not rely on either intra-camera or inter-camera object tracking; it thus can be applied to low-quality surveillance videos featured with severe inter-object occlusions. The effectiveness and robustness of our approach are demonstrated through experiments on 330 hours of videos captured from 17 cameras installed at two busy underground stations with complex and diverse scenes.