There is a strong demand of smart vision based surveillance system owing to the increase in crime at a frightening rate at various public places like Banks, Airport, Shopping malls and its application in human activity recognition ranges from patient fall detection, irregular pattern recognition or Human computer Interaction. As the crime increases at a disturbing rate, public security violations and high cost of security personals have motivated the author to do the strategic survey of existing vision and image processing based techniques in the past literature. The paper begins with discussing the common approach towards suspicious activity detection and recognition followed by summarizing the supervised and unsupervised machine learning methodologies mainly based on SVM, HMM and ANN classifiers, which were adopted by the researchers previously varying from single human behavior modeling to crowded scenes. Next, this paper discusses system model for human’s normal and abnormal activities recognition along with various feature selectors and detectors used in previous literature. This was followed by conducting a review of benchmark researches which covered a comprehensive state of art methodologies in the related fields, key points owned, feature learning and applications. At last experimental aspects of various papers have been discussed with essential performance matrices like accuracy along with the major issues, common problems, challenges and future scope in the related field.

A review of supervised and unsupervised machine learning techniques for suspicious behavior recognition in intelligent surveillance system

Semantic trajectory insights for worker safety in dynamic environments

Visual surveillance System is used for analysis and interpretation of object behaviors. It involves object classification to understand the visual events in videos. In this review paper various object classification methods are used. Classification technique plays an important role in surveillance system that is used for the classification of both objects like static and moving objects in a better way. The methods in object classification are used to extract meaningful information and various features that are needed for representation of data. In this survey, we described various approaches for moving objects that are used in classification for video surveillance system based on shape and motion. Index Terms—Video Surveillance, object classification, Feature extraction, neural network, recognition.

/pdf/a-study-on-classification-for-static-and-moving-object-in-37ka1yq53a.pdf

A Study on Classification for Static and Moving Object in Video Surveillance System

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/ipr2.12208

Falling motion detection algorithm based on deep learning

Predicting human mobility has always been an important task in Location-based Social Network. Previous efforts fail to capture spatial dependence effectively, mainly reflected in weakening the location topology information. In this paper, we propose a neural network-based method which can capture spatial-temporal dependence to predict the next location of a person. Specifically, we involve a graph convolutional network (GCN) based on a seq2seq framework to capture the location topology information and temporal dependence, respectively. The encoder of the seq2seq framework first generates the hidden state and cell state of the historical trajectories. The GCN is then used to generate graph embeddings of the location topology graph. Finally, we predict future trajectories by aggregated temporal dependence and graph embeddings in the decoder. For evaluation, we leverage two real-world datasets, Foursquare and Gowalla. The experimental results demonstrate that our model has a better performance than the compared models.

Next Location Prediction with a Graph Convolutional Network Based on a Seq2seq Framework

Nowadays, the human behavior analysis by computer vision techniques has been an interesting issue for researchers. Automatic recognition of actions in video allows automation of many otherwise manually intensive tasks such as video surveillance. Video surveillance system especially for elderly care and their behavior analysis has an important role to take care of aged, impatient or bedridden persons. In this paper, we propose a high accuracy human action classification and recognition method using hidden Markov model classifier. In our approach, first, we use star skeleton feature extraction method to extract extremities of human body silhouette to produce feature vectors as inputs of hidden Markov model classifier. Then, hidden Markov model, which is learned and used in our proposed surveillance system, classifies the investigated behaviors and detects abnormal actions with high accuracy in comparison by other abnormal detection reported in previous works. The accuracy about 94% resulted from confusion matrix approve the efficiency of the proposed method when compared with its counterparts for abnormal action detection.

/pdf/recognition-and-classification-of-human-behavior-in-plj3jke43f.pdf

Recognition and Classification of Human Behavior in Intelligent Surveillance Systems using Hidden Markov Model

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Recognition and Classification of Human Behavior in Intelligent Surveillance Systems using Hidden Markov Model