Video surveillance is rapidly increasing meaningful approach for deterrent to crime, and ability to apprehend a suspect when a crime occurs. Before few years video surveillance paired with security guards which means security guards should watch surveillance TV at 24/7 days it made more pricy. But nowadays it’s turned into automatic which gives good safety with low budget. Normally Surveillance...

In this paper, we propose a method to summarize an egocentric moving video (a video recorded by a moving wearable camera) for generating a walking route guidance video. To summarize an egocentric video, we analyze it by applying pedestrian crosswalk detection as well as ego-motion classification, and estimate an importance score of each section of the given video. Based on the estimated importa...

Recently, various conferences and journals have published articles related to Video Surveillance Systems, indicating researchers‘ attention. The goal of this review is to examine the latest works were published in journals, propose a new classification framework of video surveillance systems and investigate each aspect of this classification framework. This paper provides a comprehensive and sy...

This paper proposes three techniques of feature extraction for person independent action classification in compressed MPEG video. The features used are extracted from motion vectors, obtained by partial decoding of the MPEG video. The feature vectors are fed to Hidden Markov Model (HMM) for classification of actions. Totally seven actions were trained with distinct HMM for classification. Recog...

In this paper, we explore supervised classification methods for video shot segmentation. We transform the temporal segmentation problem into a multi-class categorization issue. This approach provides a uniform framework for using different kinds of features extracted from the video and for detecting various types of shot boundaries. The approach utilizes manual labeled training data and a simpl...

In this paper, we investigate the problem of video classification into predefined genre. The approach adopted is based on spatial and temporal descriptors derived from short video sequences (20 seconds). By using support vector machines (SVMs), we propose an optimized multiclass classification method. Five popular TV broadcast genre namely cartoon, commercials, cricket, football and tennis are ...

In this paper, we present our solution to Google YouTube-8M Video Classification Challenge 2017. We leveraged both video-level and frame-level features in the submission. For video-level classification, we simply used a 200-mixture Mixture of Experts (MoE) layer, which achieves GAP 0.802 on the validation set with a single model. For frame-level classification, we utilized several variants of r...

In this work we discuss an emotional classification of videos based on users physiological signals and video low-level processing. This kind of automatic user classification has the potential to increase the naturalness of interaction. Everyday the number of online videos and films is increasingly available. The emotional dimensions of videos require specific tools to enable video access and se...

To support more effective video retrieval at semantic level, we introduce a novel framework to achieve semantic video classification. This novel framework includes: (a) A semantic-senstive video content representation framework via principal video shots to enhance the quality of features (i.e., the ability of the selected low-level multimodal perceptual features to discriminate among various se...

This paper presents a Learning Vector Quantization (LVQ)-based temporal tracking method for semi-automatic video object segmentation. A semantic video object is initialized using user assistance in a reference frame to give initial classification of video object and its background regions. The LVQ training approximates video object and background classification and use them for automatic segmen...