The effectiveness of face detection algorithms in unconstrained crowd scenes

The 2013 Boston Marathon bombing represents a case where automatic facial biometrics tools could have proven invaluable to law enforcement officials, yet the lack of ro-bustness of current tools in unstructured environments lim-ited their utility. In this work, we focus on complications that confound face detection algorithms. We first present a simple multi-pose generalization of the Viola-Jones al-gorithm. Our results on the Face Detection Data set and Benchmark (FDDB) show that it makes a significant im-provement over the state of the art for published algorithms. Conversely, our experiments demonstrate that the improve-ments attained by accommodating multiple poses can be negligible compared to the gains yielded by normalizing scores and using the most appropriate classifier for uncon-trolled data. We conclude with a qualitative evaluation of the proposed algorithm on publicly available images of the Boston Marathon crowds. Although the results of our evalu-ations are encouraging, they confirm that there is still room for improvement in terms of robustness to out-of-plane ro-tation, blur and occlusion. 1

Synthetic Data for Video Surveillance Applications of Computer Vision: A Review

In recent years, there has been a growing interest in synthetic data for several computer vision applications, such as automotive, detection and tracking, surveillance, medical image analysis and robotics. Early use of synthetic data was aimed at performing controlled experiments under the analysis by synthesis approach. Currently, synthetic data are mainly used for training computer vision models, especially deep learning ones, to address well-known issues of real data, such as manual annotation effort, data imbalance and bias, and privacy-related restrictions. In this work, we survey the use of synthetic training data focusing on applications related to video surveillance, whose relevance has rapidly increased in the past few years due to their connection to security: crowd counting, object and pedestrian detection and tracking, behaviour analysis, person re-identification and face recognition. Synthetic training data are even more interesting in this kind of application, to address further, specific issues arising, e.g., from typically unconstrained image or video acquisition conditions and cross-scene application scenarios. We categorise and discuss the existing methods for creating synthetic data, analyse the synthetic data sets proposed in the literature for each of the considered applications, and provide an overview of their effectiveness as training data. We finally discuss whether and to what extent the existing synthetic data sets mitigate the issues of real data, highlight existing open issues, and suggest future research directions in this field

OPML: A One-Pass Closed-Form Solution for Online Metric Learning

To achieve a low computational cost when performing online metric learning for large-scale data, we present a one-pass closed-form solution namely OPML in this paper. Typically, the proposed OPML first adopts a one-pass triplet construction strategy, which aims to use only a very small number of triplets to approximate the representation ability of whole original triplets obtained by batch-manner methods. Then, OPML employs a closed-form solution to update the metric for new coming samples, which leads to a low space (i.e., $O(d)$) and time (i.e., $O(d^2)$) complexity, where $d$ is the feature dimensionality. In addition, an extension of OPML (namely COPML) is further proposed to enhance the robustness when in real case the first several samples come from the same class (i.e., cold start problem). In the experiments, we have systematically evaluated our methods (OPML and COPML) on three typical tasks, including UCI data classification, face verification, and abnormal event detection in videos, which aims to fully evaluate the proposed methods on different sample number, different feature dimensionalities and different feature extraction ways (i.e., hand-crafted and deeply-learned). The results show that OPML and COPML can obtain the promising performance with a very low computational cost. Also, the effectiveness of COPML under the cold start setting is experimentally verified.Comment: 12 page