Anti-social behavior detection in audio-visual surveillance systems

In this paper we propose a general purpose framework for detection of unusual events. The proposed system is based on the unsupervised method for unusual scene detection in web{cam images that was introduced in [1]. We extend their algorithm to accommodate data from different modalities and introduce the concept of time-space blocks. In addition, we evaluate early and late fusion techniques for our audio-visual data features. The experimental results on 192 hours of data show that data fusion of audio and video outperforms using a single modality

PhD Forum: Investigating the performance of a multi-modal approach to unusual event detection

In this paper, we investigate the parameters under- pinning our previously presented system for detecting unusual events in surveillance applications [1]. The system identifies anomalous events using an unsupervised data-driven approach. During a training period, typical activities within a surveilled environment are modeled using multi-modal sensor readings. Significant deviations from the established model of regular activity can then be flagged as anomalous at run-time. Using this approach, the system can be deployed and automatically adapt for use in any environment without any manual adjustment. Experiments carried out on two days of audio-visual data were performed and evaluated using a manually annotated ground- truth. We investigate sensor fusion and quantitatively evaluate the performance gains over single modality models. We also investigate different formulations of our cluster-based model of usual scenes as well as the impact of dynamic thresholding on identifying anomalous events. Experimental results are promis- ing, even when modeling is performed using very simple audio and visual features

Fast Fight Detection

Action recognition has become a hot topic within computer vision. However, the action recognition community has focused mainly on relatively simple actions like clapping, walking, jogging, etc. The detection of specific events with direct practical use such as fights or in general aggressive behavior has been comparatively less studied. Such capability may be extremely useful in some video surveillance scenarios like prisons, psychiatric centers or even embedded in camera phones. As a consequence, there is growing interest in developing violence detection algorithms. Recent work considered the well-known Bag-of-Words framework for the specific problem of fight detection. Under this framework, spatio-temporal features are extracted from the video sequences and used for classification. Despite encouraging results in which high accuracy rates were achieved, the computational cost of extracting such features is prohibitive for practical applications. This work proposes a novel method to detect violence sequences. Features extracted from motion blobs are used to discriminate fight and non-fight sequences. Although the method is outperformed in accuracy by state of the art, it has a significantly faster computation time thus making it amenable for real-time applications

Police Body Worn Cameras and Privacy: Retaining Benefits While Reducing Public Concerns

Recent high-profile incidents of police misconduct have led to calls for increased police accountability. One proposed reform is to equip police officers with body worn cameras, which provide more reliable evidence than eyewitness accounts. However, such cameras may pose privacy concerns for individuals who are recorded, as the footage may fall under open records statutes that would require the footage to be released upon request. Furthermore, storage of video data is costly, and redaction of video for release is time-consuming. While exempting all body camera video from release would take care of privacy issues, it would also prevent the public from using body camera footage to uncover misconduct. Agencies and lawmakers can address privacy problems successfully by using data management techniques to identify and preserve critical video evidence, and allowing non-critical video to be deleted under data-retention policies. Furthermore, software redaction may be used to produce releasable video that does not threaten the privacy of recorded individuals

Object Tracking in Audio-Visual Scene

The thesis aims at tracking of an object using visual and audio information simultaneously. Presently such kind of systems have limited use in traffic monitoring, security surveillance systems, spying and espionage etc. After substantial literature review it was found that Audio-Visual fusion is one of the techniques yet to be explored for object tracking due to the difficulty involved in synchronizing both the modalities viz audio and video. The advantage of such a system is that it is more robust in challenging environments. The aim is to overcome the shortcomings that are found in standalone audio and video techniques. In environments where low lighting conditions are found, the video techniques fail to track the object since the installed cameras don’t receive the proper images but the audio tracking assists in such cases. Similarly, we might have situations in which there is a lot of background noise, room reverberations or any combination of the above factors. In such a situation, audio tracking fails but video tracking works robustly. In this thesis, a colour based tracking approach using particle filter has been implemented by entering the gray scale indices of the red, green and blue planes of the object’s image into the system as the input. Then an audio source localization method based on Simplex Optimization is implemented. This approach is based on capturing the sound signals from a scene by a set of microphones and then locating the source location by using time delay estimation techniques and simplex optimization. Then an attempt to fuse the information obtained from both modes has been studied