The automatic detection of anomalies captured by surveillance settings is essential for speeding the otherwise laborious approach. To date, UCF-Crime is the largest available dataset for automatic visual analysis of anomalies and consists of real-world crime scenes of various categories. In this paper, we introduce HR-Crime, a subset of the UCF-Crime dataset suitable for human-related anomaly detection tasks. We rely on state-of-the-art techniques to build the feature extraction pipeline for human-related anomaly detection. Furthermore, we present the baseline anomaly detection analysis on the HR-Crime. HR-Crime as well as the developed feature extraction pipeline and the extracted features will be publicly available for further research in the field.
People use clothing to make personality inferences about others, and these inferences steer social behaviors. The current work makes four contributions to the measurement and prediction of clothing-based person perception: first, we integrate published research and open-ended responses to identify common psychological inferences made from clothes (Study 1). We find that people use clothes to make inferences about happiness, sexual interest, intelligence, trustworthiness, and confidence. Second, we examine consensus (i.e., interrater agreement) for clothing-based inferences (Study 2). We observe that characteristics of the inferring observer contribute more to the drawn inferences than the observed clothes, which entails low to medium levels of interrater agreement. Third, the current work examines whether a computer vision model can use image properties (i.e., pixels alone) to replicate human inferences (Study 3). While our best model outperforms a single human rater, its absolute performance falls short of reliability conventions in psychological research. Finally, we introduce a large database of clothing images with psychological labels and demonstrate its use for exploration and replication of psychological research. The database consists of 5000 images of (western) women’s clothing items with psychological inferences annotated by 25 participants per clothing item.
In this work, we address the problem of estimating the so-called "Social Distancing" given a single uncalibrated image in unconstrained scenarios. Our approach proposes a semi-automatic solution to approximate the homography matrix between the scene ground and image plane. With the estimated homography, we then leverage an off-the-shelf pose detector to detect body poses on the image and to reason upon their inter-personal distances using the length of their body-parts. Inter-personal distances are further locally inspected to detect possible violations of the social distancing rules. We validate our proposed method quantitatively and qualitatively against baselines on public domain datasets for which we provided groundtruth on interpersonal distances. Besides, we demonstrate the application of our method deployed in a real testing scenario where statistics on the inter-personal distances are currently used to improve the safety in a critical environment.
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