The detection of violent behavior in the public environment using video content has become increasingly important in recent years due to the rise of violent incidents and the ease of sharing and disseminating video content through social media platforms. Efficient and effective techniques for detecting violent behavior in video content can assist authorities with identifying potential hazards, preventing crimes, and promoting public safety. Violence detection can also help to mitigate the psychological damage caused by viewing violent content, particularly in vulnerable populations such as infants and victims of violence. We have proposed an algorithm to calculate new descriptors using the magnitude and orientation of optical flow (MOOF) in the video. Descriptors are extracted from MOOF based on four binary histograms each by applying various weighted thresholds. These descriptors are used to train Support Vector Machine (SVM) and classify the video as violent or nonviolent. The proposed algorithm has been tested on the publicly available Hockey Fight Dataset and Violent Flow dataset. The results demonstrate that the proposed descriptors outperform the state-of-the-art algorithms with an accuracy of 91.5% and 78.5% on the Hockey Fight and Violent Flow datasets, respectively.

The risk of cardiovascular diseases is growing worldwide, and its early detection is necessary to reduce the level of risk. Structural parameters of the carotid artery as intima-media thickness and functional parameters such as arterial elasticity are directly associated with cardiovascular diseases. Segmentation of the carotid artery is required to measure the structural parameters and its temporal value that is used to estimate the arterial elasticity. This paper has two primary objectives: (i) Segmentation of the sequence of carotid artery ultrasound to measure temporal value of intima-media thickness and lumen-diameter, and (ii) Young’s modulus of elasticity estimation. The proposed segmentation method uses the contextual feature of the image pattern and is based on multi-layer extreme learning machine auto-encoder network. This segmentation method has two parts: (a) region of interest localization and (b) lumen-intima interface and media-adventitia interface detection at the far wall. ROI localization algorithm divides the ultrasound frame into columns and also divides each column into overlapping blocks, ensuring that every column has a region of interest block. A multi-layer extreme learning machine with auto-encoder is trained with labelled data and in testing; system classifies the blocks into ‘region of interest’ and ‘non-region of interest’. Pixels belonging to the region of interest are classified in the first part and a similar network-based method is proposed for lumen-intima and media-adventitia interface detection at the near wall of the carotid artery. Structural parameter of the artery, intima-media thickness and lumen diameter are measured in a sequence of images of the cardiac cycle. The temporal values of structural parameters are used to estimate the young’s modulus of elasticity.