Context-Aware Surveillance Video Summarization

Context-Aware Video Summarization

We present a method that is able to find the most informative video portions, leading to a summarization of video sequences. In contrast to the existing works, our method is able to capture the important video portions through information about individual local motion regions, as well as the interactions between these motion regions. Specifically, our proposed Context-Aware Video Summarization ...

Diversity-aware Multi-Video Summarization (Supplementary Material)

Long-Term Identity-Aware Multi-Person Tracking for Surveillance Video Summarization

In multi-person tracking scenarios, gaining access to the identity of each tracked individual is crucial for many applications such as long-term surveillance video analysis. Therefore, we propose a long-term multi-person tracker which utilizes face recognition information to not only enhance tracking performance, but also assign identities to tracked people. As face recognition information is n...

Fully Context-Aware Video Prediction

This paper proposes a new neural network design for unsupervised learning through video prediction. Current video prediction models based on convolutional networks, recurrent networks, and their combinations often result in blurry predictions. Recent work has attempted to address this issue with techniques like separation of background and foreground modeling, motion flow learning, or adversari...

Towards crowd density-aware video surveillance applications

Crowd density analysis is a crucial component in visual surveillance mainly for security monitoring. This paper proposes a novel approach for crowd density measure, in which local information at pixel level substitutes a global crowd level or a number of people per-frame. The proposed approach consists of generating automatic crowd density maps using local features as an observation of a probab...