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Highlights: [•] Focus of interests in videos is localized by 3D spatiotemporal visual attention modeling. [•] Salient feature points are detected in visual attention regions. [•] Geometric constraint measurement is used for conducting bi-directional point matching. [•] Video matching is transformed to frame-based time-series linear search problem. [•] Our proposed approach achieves promising high detection rate. [ABSTRACT FROM AUTHOR]

Abstract: In nighttime driving brake lights are particularly important because they offer a warning signal to prevent potential collisions. In this work, we propose a novel visual-based approach for nighttime brake light detection using three-dimensional Nakagami imaging to analyze tail lights of vehicles in front. Rather than heuristic features, such as symmetry of taillights and appearance of the third brake light, the proposed approach extracts invariant features by modeling the scattering of brake lights, thus allowing detection to proceed in a part-based manner. Experiments from extensive datasets show that the proposed system can effectively detect vehicle braking under different lighting and traffic conditions, making it a realistic option for real-world applications. [Copyright &y& Elsevier]

Abstract: State of the art methods for video resizing usually produce perceivable visual discontinuities. Therefore, how to preserve the visual continuity in video frames is one of the most critical issues. In this paper, we propose a novel approach for modeling dynamic visual attention based on spatiotemporal analysis in order to detect the focus of interest automatically. The continuously varied co-sited blocks in a video cube are first detected and their variations are characterized as visual cubes, which are further employed to determine a proper extent of salient regions in video frames. Once the proper extent through video cubes is determined, the resizing process then can be conducted to find the global optimum. Our experiment shows that the proposed content-aware video resizing based on spatiotemporal visual cubes can effectively generate resized videos while keeping their isotropic manipulation and the continuous dynamics of visual perception. [Copyright &y& Elsevier]

Abstract: Analyzing human crowds is an important issue in video surveillance and is a challenging task due to their nature of non-rigid shapes. In this paper, optical flows are first estimated and then used for a clue to cluster human crowds into groups in unsupervised manner using our proposed method of adjacency-matrix based clustering (AMC). While the clusters of human crowds are obtained, their behaviors with attributes, orientation, position and crowd size, are characterized by a model of force field. Finally, we can predict the behaviors of human crowds based on the model and then detect if any anomalies of human crowd(s) present in the scene. Experimental results obtained by using extensive dataset show that our system is effective in detecting anomalous events for uncontrolled environment of surveillance videos. [Copyright &y& Elsevier]

This paper proposes a novel system to analyze human-object interaction events happening between hands and faces in real time. Two challenging problems in this event analysis must be addressed, i.e. , there is no prior knowledge (like shape, color, size, and texture) about the handheld objects, and there are large spatial–temporal variations in event representation. For the first challenge, a novel ratio histogram is proposed to find important color bins to locate handheld objects and their trajectories via a code book technique. This scheme is different from other boosted methods which require very time-consuming estimations to search reliable body configurations. For the second challenge, a mixture of HMMs is proposed to describe an event not only from its dynamic context but also its multiplicity context. It can be performed in real time because an exhaustive search process is avoided to find possible interaction pairs between objects and body parts. [ABSTRACT FROM AUTHOR]