Learning to Detect Violent Videos using Convolutional Long Short-Term Memory

Developing a technique for the automatic analysis of surveillance videos in order to identify the presence of violence is of broad interest. In this work, we propose a deep neural network for the purpose of recognizing violent videos. A convolutional neural network is used to extract frame level features from a video. The frame level features are then aggregated using a variant of the long short term memory that uses convolutional gates. The convolutional neural network along with the convolutional long short term memory is capable of capturing localized spatio-temporal features which enables the analysis of local motion taking place in the video. We also propose to use adjacent frame differences as the input to the model thereby forcing it to encode the changes occurring in the video. The performance of the proposed feature extraction pipeline is evaluated on three standard benchmark datasets in terms of recognition accuracy. Comparison of the results obtained with the state of the art techniques revealed the promising capability of the proposed method in recognizing violent videos.Comment: Accepted in International Conference on Advanced Video and Signal based Surveillance(AVSS 2017

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

Contributions to the Problem of Fight Detection in Video

While action detection has become an important line of research in computer vision, the detection of particular events such as violence, aggression or fights, has been relatively less studied. These tasks may be extremely useful in several video surveillance scenarios such as psychiatric wards, prisons or even in camera smartphones. The clear practical applications have led to a surge of interest in developing violence detectors

Contributions to the Problem of Fight Detection in Video

While action detection has become an important line of research in computer vision, the detection of particular events such as violence, aggression or fights, has been relatively less studied. These tasks may be extremely useful in several video surveillance scenarios such as psychiatric wards, prisons or even in camera smartphones. The clear practical applications have led to a surge of interest in developing violence detectors

A machine learning approach to detect violent behaviour from video

The automatic classification of violent actions performed by two or more persons is an important task for both societal and scientific purposes. In this paper, we propose a machine learning approach, based a Support Vector Machine (SVM), to detect if a human action, captured on a video, is or not violent. Using a pose estimation algorithm, we focus mostly on feature engineering, to generate the SVM inputs. In particular, we hand-engineered a set of input features based on keypoints (angles, velocity and contact detection) and used them, under distinct combinations, to study their effect on violent behavior recognition from video. Overall, an excellent classification was achieved by the best performing SVM model, which used keypoints, angles and contact features computed over a 60 frame image input range.Fundação para a Ciência e a Tecnologia (UID/CEC/00319/2013

Discovery and recognition of motion primitives in human activities

We present a novel framework for the automatic discovery and recognition of motion primitives in videos of human activities. Given the 3D pose of a human in a video, human motion primitives are discovered by optimizing the `motion flux', a quantity which captures the motion variation of a group of skeletal joints. A normalization of the primitives is proposed in order to make them invariant with respect to a subject anatomical variations and data sampling rate. The discovered primitives are unknown and unlabeled and are unsupervisedly collected into classes via a hierarchical non-parametric Bayes mixture model. Once classes are determined and labeled they are further analyzed for establishing models for recognizing discovered primitives. Each primitive model is defined by a set of learned parameters. Given new video data and given the estimated pose of the subject appearing on the video, the motion is segmented into primitives, which are recognized with a probability given according to the parameters of the learned models. Using our framework we build a publicly available dataset of human motion primitives, using sequences taken from well-known motion capture datasets. We expect that our framework, by providing an objective way for discovering and categorizing human motion, will be a useful tool in numerous research fields including video analysis, human inspired motion generation, learning by demonstration, intuitive human-robot interaction, and human behavior analysis

Automatic video censoring system using deep learning

Due to the extensive use of video-sharing platforms and services, the amount of such all kinds of content on the web has become massive. This abundance of information is a problem controlling the kind of content that may be present in such a video. More than telling if the content is suitable for children and sensitive people or not, figuring it out is also important what parts of it contains such content, for preserving parts that would be discarded in a simple broad analysis. To tackle this problem, a comparison was done for popular image deep learning models: MobileNetV2, Xception model, InceptionV3, VGG16, VGG19, ResNet101 and ResNet50 to seek the one that is most suitable for the required application. Also, a system is developed that would automatically censor inappropriate content such as violent scenes with the help of deep learning. The system uses a transfer learning mechanism using the VGG16 model. The experiments suggested that the model showed excellent performance for the automatic censoring application that could also be used in other similar applications