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Camera-equipped mobile devices are encouraging people to take more photos and the development and growth of social networks is making it increasingly popular to share photos online. When objects appear in overlapping Fields Of View (FOV), this means that they are drawing much attention and thus indicates their popularity. Successfully discovering and locating these objects can be very useful for many applications, such as criminal investigations, event summaries, and crowdsourcing-based Geographical Information Systems (GIS). Existing methods require either prior knowledge of the environment or intentional photographing. In this paper, we propose a seamless approach called "Spotlight", which performs passive localization using crowdsourced photos. Using a graph-based model, we combine object images across multiple camera views. Within each set of combined object images, a photographing map is built on which object localization is performed using plane geometry. We evaluate the system's localization accuracy using photos taken in various scenarios, with the results showing our approach to be effective for passive object localization and to achieve a high level of accuracy.

It is still a challenge to detect anomalous events in video sequences in the field of computer vision due to heavy object occlusions, varying crowded densities and complex situations. To address this, we propose a novel human-machine cooperative approach which uses human feedback on anomaly confirmation to inform and enhance video anomaly detection. Specifically, we analyze the spatio-temporal characteristics of sequential frames of a video from the appearance and motion perspective from which spatial and temporal features are identified and extracted. We then develop a convolutional autoencoder neural network to compute an abnormal score based on reconstruction errors. In this process, a group of experts will provide human feedback to a certain proportion of classified frames to be incorporated into the model, and also the final judgment for the event anomalies for training and classification. The proposed approach is evaluated on 3 publicly available surveillance datasets, showing improved accuracy and competitive performance (93.7% AUC) with respect to the best performance (90.6% AUC) of the state-of-the-art approaches. The approach has not been previously seen to the best of our knowledge.