Suspicious Activity Detection from Surveillance Video using Deep Learning Approach
Rohit Shinde
, Sonali Suryavanshi , Akash Phad , Sarthak Kathe , Prof. S. S. Gunjal
Suspicious Activity, Video Surveillance, Deep Learning.
Video surveillance plays an important role in today's world. Artificial intelligence, machine learning, and deep learning entered his system, making the technology too advanced. Using a combination of the above, different systems are positioned to help distinguish different suspicious behavior from live tracking footage. Human behavior is the most unpredictable and it is very difficult to tell if it is suspicious ornormal. Deep learning approaches are used to detect suspiciousor normal activity in academic environments, sending alert messages to appropriate authorities when suspicious activity ispredicted. Monitoring is often performed through consecutive frames extracted from the video. The entire framework is divided into two parts. In the first part features are computed from the video image and in the second part the classifier predicts the class as suspect or normal based on the features obtained.
"Suspicious Activity Detection from Surveillance Video using Deep Learning Approach", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.8, Issue 3, page no.625 - 629, March-2023, Available :https://ijsdr.org/papers/IJSDR2303102.pdf
Volume 8
Issue 3,
March-2023
Pages : 625 - 629
Paper Reg. ID: IJSDR_204306
Published Paper Id: IJSDR2303102
Downloads: 000347242
Research Area: Computer Engineering
Country: Nashik, Maharashtra, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
