Unusual event detection in surveillance videos

• Grant number: 15/04883-0
• Support Opportunities: Scholarships in Brazil - Doctorate (Direct)
• Start date: January 01, 2016
• End date: April 30, 2017
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Moacir Antonelli Ponti
• Grantee: Tiago Santana de Nazare
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Associated research grant: 11/22749-8 - Challenges in exploratory visualization of multidimensional data: paradigms, scalability and applications, AP.TEM

Abstract

Anomaly detection in video has several important applications, specially nowadays when cameras are widely available. One of the challenges in this scenario is that there is a huge amount of data (video frames) and only small part of it is important (unusual/abnormal events). Consequently, the detection of these anomalous events by humans is a tedious and ineffective process. This situation has motivated research on automatic systems to assist the detection of abnormal events in surveillance videos. Despite the efforts and advances obtained lately, most state-of-the-art methods still are not able to achieve real time processing. Besides that, most of these methods are not able to continue learning after the training phase ends, which means that they may not deal well with changes like lightning variations. This project aims at improving unusual event detection in surveillance videos by investigating both anomaly detection algorithms and spatio-temporal descriptors. Regarding anomaly detection algorithms this project will investigate methods that can continue to learn after the training phase ends. Concerning spatio-temporal descriptors this project will investigate, with help of visualization techniques, descriptors that take spatio-temporal context into consideration. (AU)

Scientific publications

(5)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

CONTATO, WELINTON A.; NAZARE, TIAGO S.; PARANHOS DA COSTA, GABRIEL B.; PONTI, MOACIR; BATISTA NETO, JOAO E. S.; IEEE. Improving Non-Local Video Denoising with Local Binary Patterns and Image Quantization. 2016 29TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), v. N/A, p. 8-pg., 2016-01-01. (15/05310-3, 14/21888-2, 15/04883-0)

NAZARE, TIAGO S.; DE MELLO, RODRIGO F.; PONTI, MOACIR A.; FARINELLA, GM; RADEVA, P; BRAZ, J; BOUATOUCH, K. Investigating 3D Convolutional Layers as Feature Extractors for Anomaly Detection Systems Applied to Surveillance Videos. VISAPP: PROCEEDINGS OF THE 16TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS - VOL. 5: VISAPP, v. N/A, p. 10-pg., 2021-01-01. (13/07375-0, 15/04883-0, 18/22482-0)

NAZARE, TIAGO S.; PARANHOS DA COSTA, GABRIEL B.; DE MELLO, RODRIGO F.; PONTI, MOACIR A.; IEEE. Color quantization in transfer learning and noisy scenarios: an empirical analysis using convolutional networks. PROCEEDINGS 2018 31ST SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), v. N/A, p. 7-pg., 2018-01-01. (15/05310-3, 17/16548-6, 13/07375-0, 15/04883-0, 16/16111-4)

PONTI, MOACIR; NAZARE, TIAGO S.; KITTLER, JOSEF; IEEE. OPTICAL-FLOW FEATURES EMPIRICAL MODE DECOMPOSITION FOR MOTION ANOMALY DETECTION. 2017 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), v. N/A, p. 5-pg., 2017-01-01. (15/04883-0)

PONTI, MOACIR A.; RIBEIRO, LEONARDO S. F.; NAZARE, TIAGO S.; BUI, TU; COLLOMOSSE, JOHN; IEEE. Everything you wanted to know about Deep Learning for Computer Vision but were afraid to ask. 2017 30TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES TUTORIALS (SIBGRAPI-T), v. N/A, p. 25-pg., 2017-01-01. (13/07375-0, 17/10068-2, 15/04883-0)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

NAZARE, Tiago Santana de. Unusual Event Detection in Surveillance Videos. 2020. Doctoral Thesis - Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) São Carlos.