Project Title: Timestamp aware Aberrant Detection and Analysis in Big Visual Data using Deep Learning Architecture

Funding Agency: Science and Engineering Research Board, Department of Science and Technology (SERB-DST, 2018)
Principal Investigator: Dr. Santosh Kumar Vipparthi
JRF/Ph.D. Scholar: Kuldeep Marotirao Biradar
The proposed system removes the onus of detecting aberrance situations from the manual operator; and rather, places it on the video surveillance system. “The present technologies are fails to recognize aberration in video sequences. These aberrances occur over a small-time window. Thus, recognizing with its timeframe from a big visual data is really challenging task”. Hence, “our focus is on problems, where we are given a set of nominal training videos samples. Based on these samples need to determine whether or not a test video contains an aberration and what instant it occurs”. Similarly, “we aim to significantly reduce the time and human effort by automating the task and improving the accuracy by recognizing aberrances with its timestamp”. Further, “exploit the aberrance activity of the object by modeling the rich motion patterns in selected region, effectively capturing the underlying intrinsic structure they form in the video”. This system can be applied in various areas such as security systems, intelligent agencies, banks, department stores, traffic monitoring on highway, airport terminal check-in, sports, medical field, and robotics etc.
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New Anomaly Dataset

A custom dataset was generated in a staged/controlled environment. We shot from four strategically placed cameras simultaneously to capture multiple views of same scene. The videos were recorded at four different locations in different times of the day. The scenes involve normal data, fight happening in different scenarios, snatching, kidnapping etc. Scenes were shot indoor/outdoor, in natural light-artificial light, low light as well to cover illumination changes. The videos were recorded from varied distances to capture subjects with varying size. Post processing yielded usable clips of approx. 90 minutes (90x60x30x4= 648000 frames). Snippet for the same are depicted in the figure.
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Publications

1. Murari Mandal, Monu Verma, Sonakshi Mathur, Santosh Vipparthi, Subrahmanyam Murala, Kranthi Deveerasetty, "RADAP: Regional Adaptive Affinitive Patterns with Logical Operators for Facial Expression Recognition," IET Image Processing, 2019(Impact Factor 1.40).
2.Murari Mandal, Santosh Kumar Vipparthi, Mallika Chaudhary, Subramanian Murala, Anil Balaji Gonde, S. K. Nagar, ANTIC: ANTithetic Isomeric Cluster Patterns for Medical Image Retrieval and Change Detection, IET Computer Vision, (IEEE, IET), (2018) (Impact Factor 1.087).
3.Murari Mandal, Prafulla Saxena, Santosh Kumar Vipparthi, Subrahmanyam Murala, CANDID: Robust Change Dynamics and Deterministic Update Policy for Dynamic Background Subtraction, IEEE 24th International Conference on Pattern Recognition (ICPR), Beijing, China (IEEE) (2018).
4.Monu Verma, Jaspreet Kaur Bhui, Santosh Kumar Vipparthi, Girdhari Singh, EXPERTNet: Exigent Features Preservative Network for Facial Expression Recogntion. ACM 11th International Conference on Computer Vision, Graphics and Image Processing (ICVGIP), Hyderabad, India (2018).
5.Kuldeep Biradar, Sachin Dube, Santosh Kumar Vipparthi, “DEAREST: Deep Convolutional Aberrant Behaviour Detection in Real world Scenario,” 13th international conference on industrial and information system, 2018.
6.Monu Verma, Prafulla Saxena, Santosh Kumar Vipparthi, Gridhari Singh, QUEST: Quadriletral Senary bit Pattern for Facial Expression Recognition, IEEE International Conference on Systems, Man, and Cybernatics, Miyazaki, Japan, (IEEE) (2018).