Anomaly Detection in Videos for Video Surveillance Applications using Neural Networks

Security is always a main concern in every domain, due to a rise in crime rate in the crowded event or suspicious lonely areas. Abnormal detection and monitoring have major applications of computer vision to tackle various problems. Due to growing demand in the protection of safety, security and personal properties, the needs and deployment of video surveillance systems can recognize and interpret the scene and anomaly events play a vital role in intelligence monitoring. Anomaly detection is a technique used to distinguish various patterns and identify unusual patterns with a minimal period, this pattern is called outliers. Surveillance videos can capture a variety of realistic anomalies. Anomaly detection in video surveillance involves breaking down the whole process into three layers, which are video labelers, image processing, and activity detection. Hence, anomaly detection in videos for video surveillance application gives assured results in regards to real-time scenarios. In this paper, we anomaly was detected in images and videos with an accuracy of 98.5 %.

[1]  Mohana,et al.  Implementation of real time moving object detection and tracking on FPGA for video surveillance applications , 2016, 2016 IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER).

[2]  Michael Teutsch,et al.  A Benchmark for Deep Learning Based Object Detection in Maritime Environments , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[3]  Tong Lu,et al.  Anomaly detection with spatio-temporal context using depth images , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[4]  Rongrong Ni,et al.  Anomaly Detection in Moving Crowds through Spatiotemporal Autoencoding and Additional Attention , 2018, Adv. Multim..

[5]  Mohana,et al.  Elegant and efficient algorithms for real time object detection, counting and classification for video surveillance applications from single fixed camera , 2016, 2016 International Conference on Circuits, Controls, Communications and Computing (I4C).

[6]  Andreas E. Savakis,et al.  Anomaly Detection in Video Using Predictive Convolutional Long Short-Term Memory Networks , 2016, ArXiv.

[7]  Mohana,et al.  Classification of Objects in Video Records using Neural Network Framework , 2018, 2018 International Conference on Smart Systems and Inventive Technology (ICSSIT).

[8]  Yuan-Kai Wang,et al.  Traffic Camera Anomaly Detection , 2014, 2014 22nd International Conference on Pattern Recognition.

[9]  Abul Bashar,et al.  SURVEY ON EVOLVING DEEP LEARNING NEURAL NETWORK ARCHITECTURES , 2019, December 2019.

[10]  Mohana,et al.  Feature Extraction using Convolution Neural Networks (CNN) and Deep Learning , 2018, 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT).

[11]  Meghana R K,et al.  Background-modelling techniques for foreground detection and Tracking using Gaussian Mixture Model , 2019, 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC).

[12]  Hichem Snoussi,et al.  An On-Line and Adaptive Method for Detecting Abnormal Events in Videos Using Spatio-Temporal ConvNet , 2019, Applied Sciences.

[13]  Mohana,et al.  Simulation and Performance Analysis of Feature Extraction and Matching Algorithms for Image Processing Applications , 2019, 2019 International Conference on Intelligent Sustainable Systems (ICISS).

[14]  Mohana,et al.  YOLO based Detection and Classification of Objects in video records , 2018, 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT).

[15]  Mohana,et al.  Real-time implementation of object detection and tracking on DSP for video surveillance applications , 2016, 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT).

[16]  Mohana,et al.  Simulation of Object Detection Algorithms for Video Survillance Applications , 2018, 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2018 2nd International Conference on.

[17]  Mohana,et al.  An area efficient FPGA implementation of moving object detection and face detection using adaptive threshold method , 2017, 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT).

[18]  Rui Dai,et al.  Blind Image Quality Prediction for Object Detection , 2019, 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR).

[19]  Mohammmad Farhadi Bajestani,et al.  AAD: Adaptive Anomaly Detection through traffic surveillance videos , 2018, ArXiv.

[20]  Mohana,et al.  Object Tracking Algorithms for Video Surveillance Applications , 2018, 2018 International Conference on Communication and Signal Processing (ICCSP).

[21]  Apoorva Raghunandan,et al.  Object Detection Algorithms for Video Surveillance Applications , 2018, 2018 International Conference on Communication and Signal Processing (ICCSP).

[22]  Mubarak Shah,et al.  Real-World Anomaly Detection in Surveillance Videos , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[23]  Mohana,et al.  A Review of Artificial Intelligence Methods for Data Science and Data Analytics: Applications and Research Challenges , 2018, 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2018 2nd International Conference on.

[24]  Mohana,et al.  Real Time Object Detection and Tracking Using Deep Learning and OpenCV , 2018, 2018 International Conference on Inventive Research in Computing Applications (ICIRCA).

[25]  Jordan Henrio,et al.  Anomaly Detection in Videos Recorded by Drones in a Surveillance Context , 2018, 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC).