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In this paper, we explore the use of a semi-supervised manifold alignment method for domain adaptation in the context of human body and head pose estimation in videos. We build upon an existing state-of-the-art system that leverages on external labelled datasets for the body and head features, and on the unlabelled test data with weak velocity labels to do a coupled estimation of the body and head...
We present a conditional random field approach to tracking-by-detection in which we model pairwise factors linking pairs of detections and their hidden labels, as well as higher order potentials defined in terms of label costs. To the contrary of previous papers, our method considers long-term connectivity between pairs of detections and models similarities as well as dissimilarities between them,...
We present a detection-based approach to multi-object tracking formulated as a statistical labeling task and solved using a Conditional Random Field (CRF) model. The CRF model relies on factors involving detection pairs and their corresponding hidden labels. These factors model pairwise position or color similarities as well as dissimilarities, and one critical issue is to be able to learn their parameters...
The automatic analysis and understanding of behavior and interactions is a crucial task in the design of socially intelligent video surveillance systems. Such an analysis often relies on the extraction of people behavioral cues, amongst which body pose and head pose are probably the most important ones. In this paper, we propose an approach that jointly estimates these two cues from surveillance video...
In surveillance videos, the task of tracking multiple people is of primary importance and is often a preliminary step before applying higher-level algorithms, e.g. to analyze interactions or to recognize behaviors. In this paper, we take a tracking-by-detection approach and formulate multi-person tracking as a statistical data association problem which seeks for the optimal label field in which detections...
In surveillance videos, cues such as head or body pose provide important information for analyzing people's behavior and interactions. In this paper we propose an approach that jointly estimates body location and body pose in monocular surveillance video. Our approach is based on tracks derived by multi-object tracking. First, body pose classification is conducted using sparse representation technique...
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