Intelligent Video Surveillance System using Deep Learning (original) (raw)
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Intelligent Video Surveillance using Deep Learning
International Journal of Engineering and Advanced Technology, 2020
Now days, Big data applications are having most of the importance and space in industry and research area. Surveillance videos are a major contribution to unstructured big data. The main objective of this paper is to give brief about video analysis using deep learning techniques in order to detect suspicious activities. Our main focus is on applications of deep learning techniques in detection the count, no of involved persons and the activity going on in a crowd considering all conditions [9]. This video analysis helps us to achieve security. Security can be defined in different terms like identification of theft, detecting violence etc. Suspicious Human Activity Detection is simply the process of detection of unusual (abnormal)l human activities . For this we need to convert the video into frames and processing these frames helps us to analyze the persons and their activities. There are two modules in this system first one Object Detection Module and Second one is Activity Detecti...
Bulletin of Electrical Engineering and Informatics, 2023
The performance of conventional surveillance systems is challenged by high error detection rates in busy scenes, which has significantly affected the accurate detection of the current surveillance system. Feature representation and object pattern extraction from different scenes have made deep learning (DL) promising methods in surveillance systems, compared to the approaches where features are created manually. To improve the detection accuracy, this paper presents an intelligent DL technique that combines convolutional neural network (CNN) and long short-term memory (LSTM). CNN extracts and learns the object features from a set of raw images, while the LSTM is then used by gated mechanisms to store important information from the extracted features. The proposed method was validated using datasets from the University of California San Diego (UCSD). The result shows that the model achieves 95% accuracy, which is superior compared to other conventional detection models.
Abnormal Activity Recognition from Surveillance Videos Using Convolutional Neural Network
Sensors (Basel, Switzerland), 2021
Background and motivation: Every year, millions of Muslims worldwide come to Mecca to perform the Hajj. In order to maintain the security of the pilgrims, the Saudi government has installed about 5000 closed circuit television (CCTV) cameras to monitor crowd activity efficiently. Problem: As a result, these cameras generate an enormous amount of visual data through manual or offline monitoring, requiring numerous human resources for efficient tracking. Therefore, there is an urgent need to develop an intelligent and automatic system in order to efficiently monitor crowds and identify abnormal activity. Method: The existing method is incapable of extracting discriminative features from surveillance videos as pre-trained weights of different architectures were used. This paper develops a lightweight approach for accurately identifying violent activity in surveillance environments. As the first step of the proposed framework, a lightweight CNN model is trained on our own pilgrim’s data...
Identification and Detection of Abnormal Human Activities using Deep Learning Techniques
2020
In recent years, it is in public to use the surveillance cameras for continuous monitoring of public and private spaces because of increasing crime. Most current surveillance systems need a human operator to constantly watch them and are ineffective as the amount of video data is increasing day by day. Surveillance cameras will be more useful tools if instead of passively recording; they generate warnings or real-time actions when unusual activity is detected. But recognizing and classifying human activity as normal or abnormal from a live video stream is a stimulating job in the pitch of CPU vision. There is a need for a smart surveillance system for the automatic identification of abnormal behaviour of humans for a specific-scene. Presentpaperstretches an overview of different machine learning methods used in recent years to develop such a model. It also gives an exposure to the recent works in the field of anomaly detection in surveillance video and its applications. Keywords—Vid...
Smart Deep Learning Based Human Behaviour Classification for Video Surveillance
Computers, materials & continua, 2022
Real-time video surveillance system is commonly employed to aid security professionals in preventing crimes. The use of deep learning (DL) technologies has transformed real-time video surveillance into smart video surveillance systems that automate human behavior classification. The recognition of events in the surveillance videos is considered a hot research topic in the field of computer science and it is gaining significant attention. Human action recognition (HAR) is treated as a crucial issue in several applications areas and smart video surveillance to improve the security level. The advancements of the DL models help to accomplish improved recognition performance. In this view, this paper presents a smart deep-based human behavior classification (SDL-HBC) model for real-time video surveillance. The proposed SDL-HBC model majorly aims to employ an adaptive median filtering (AMF) based pre-processing to reduce the noise content. Also, the capsule network (CapsNet) model is utilized for the extraction of feature vectors and the hyperparameter tuning of the CapsNet model takes place utilizing the Adam optimizer. Finally, the differential evolution (DE) with stacked autoencoder (SAE) model is applied for the classification of human activities in the intelligent video surveillance system. The performance validation of the SDL-HBC technique takes place using two benchmark datasets such as the KTH dataset. The experimental outcomes reported the enhanced recognition performance of the SDL-HBC technique over the recent state of art approaches with maximum accuracy of 0.9922.
Elsevier Measurement Sensors, 2023
Remote monitoring is the process that monitors and observes information from a distance utilizing sensors or electronic types of equipment. Remote monitoring is used in real-time applications like traffic, forest, military, shops, and hospitals to determine abnormal activities. Earlier research has done video processing methods based on computer vision techniques, but the computational complexity regarding time and memory is high. This paper designs and implements a novel Slow-Fast Convolution Neural Network (SF-CNN) to identify, detect, and classify abnormal behaviours from a surveillance video. The proposed CNN architecture learns the video frames automatically, obtains the most appropriate properties about various objects' behaviour from a large set of videos. The learning process of SF-CNN is carried out in two ways, such as slow learning and fast learning. The slow learning process is enabled when the frame rate is less, and the rapid learning process is enabled when the frame rate is high. Both the learning processes learn spatial and temporal information from the input video. Different objects, such as humans, vehicles, and animals, are detected and recognized according to their actions. All the videos have normal and abnormal activities that vary in various contexts. The proposed SF-CNN architecture provides an end-to-end solution to dealing with multiple constraints abnormal movements. The experiment is carried out on several benchmark datasets, and the performance of the SF-CNN architecture is evaluated. The proposed approach obtained 99.6% of accuracy, which is higher than the other existing techniques.
Deep Learning based Intelligent Surveillance System
International Journal of Advanced Computer Science and Applications
In the field of developing innovation, pictures are assuming as an important entity. Almost in all fields, picture base data is considered very beneficial, like in the field of security, facial acknowledgment, or therapeutic imaging, pictures make the existence simple for people. In this paper, an approach for both human detection and classification of single human activity recognition is proposed. We implement the pre-processing technique which is the fusion of the different methods. In the first step, we select the channel, apply the top hat filter, adjust the intensity values, and contrast stretching by threshold values applied to enhance the quality of the image. After pre-processing a weight-based segmentation approach is implemented to detect and compute the frame difference using cumulative mean. A hybrid feature extraction technique is used for the recognition of human action. The extracted features are fused based on serialbased fusion and later on fused features are utilized for classification. To validate the proposed algorithm 4 datasets as HOLLYWOOD, UCF101, HMDB51, and WEIZMANN are used for action recognition. The proposed technique performs better than the existing one.
Video Surveillance of Highway Traffic Events by Deep Learning Architectures
Lecture Notes in Computer Science, 2018
In this paper we describe a video surveillance system able to detect traffic events in videos acquired by fixed videocameras on highways. The events of interest consist in a specific sequence of situations that occur in the video, as for instance a vehicle stopping on the emergency lane. Hence, the detection of these events requires to analyze a temporal sequence in the video stream. We compare different approaches that exploit architectures based on Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). A first approach extracts vectors of features, mostly related to motion, from each video frame and exploits a RNN fed with the resulting sequence of vectors. The other approaches are based directly on the sequence of frames, that are eventually enriched with pixel-wise motion information. The obtained stream is processed by an architecture that stacks a CNN and a RNN, and we also investigate a transfer-learning-based model. The results are very promising and the best architecture will be tested online in real operative conditions.
Smart surveillance using deep learning
International Journal of Reconfigurable and Embedded Systems (IJRES), 2023
Smart surveillance systems play an important role in security today. The goal of security systems is to protect users against fires, car accidents, and other forms of violence. The primary function of these systems is to offer security in residential areas. In today's culture, protecting our homes is critical. Surveillance, which ranges from private houses to large corporations, is critical in making us feel safe. There are numerous machine learning algorithms for home security systems; however, the deep learning convolutional neural network (CNN) technique outperforms the others. The Keras, Tensorflow, Cv2, Glob, Imutils, and PIL libraries are used to train and assess the detection method. A web application is used to provide a user-friendly environment. The flask web framework is used to construct it. The flash-mail, requests, and telegram application programming interface (API) apps are used in the alerting approach. The surveillance system tracks abnormal activities and uses machine learning to determine if the scenario is normal or not based on the acquired image. After capturing the image, it is compared with the existing dataset, and the model is trained using normal events. When there is an anomalous event, the model produces an output from which the mean distance for each frame is calculated.
Toward trustworthy human suspicious activity detection from surveillance videos using deep learning
Soft Computing
In today’s world, suspicious or unusual activities express threats and danger to others. For the prevention of various security issues, an automatic video detection system is very important. It is difficult to consecutively monitor camera videos recorded in public places to detect any abnormal event, so an automated video detection system is needed. The study objective is to create an intelligent and trustworthy system that will take a video stream as input and detect what kind of suspicious activity is happening in that video to reduce the time that consumes watching the video. In this work, we use three models Convolutional Neural Network (CNN), GRU, and ConvLSTM model. These models are trained on the same dataset of 6 suspicious activities of humans that are: Running, Punching, Falling, Snatching, Kicking, and Shooting. The dataset consists of various videos related to each activity. Different deep learning techniques are applied in the proposed work: preprocessing, data annotati...