TY  - JOUR
AU  - Hussain, Altaf
PY  - 2025
DA  - 2025/09/21
TI  - Detection and Recognition of Real-Time Violence and Human Actions Recognition in Surveillance using Lightweight MobileNet Model
JO  - ICCK Journal of Image Analysis and Processing
T2  - ICCK Journal of Image Analysis and Processing
JF  - ICCK Journal of Image Analysis and Processing
VL  - 1
IS  - 3
SP  - 125
EP  - 146
DO  - 10.62762/JIAP.2025.839123
UR  - https://www.icck.org/article/abs/JIAP.2025.839123
KW  - real-time violence detection
KW  - CCTV surveillance video
KW  - convolutional neural networks
KW  - VGG-16
KW  - GoogLeNet
KW  - AlexNet
KW  - MobileNet
AB  - Real-time detection of violent behavior through surveillance technologies is increasingly important for public safety. This study tackles the challenge of automatically distinguishing violent from non-violent activities in continuous video streams. Traditional surveillance depends on human monitoring, which is time-consuming and error-prone, highlighting the need for intelligent systems that detect abnormal behaviors accurately with low computational cost. A key difficulty lies in the ambiguity of defining violent actions and the reliance on large annotated datasets, which are costly to produce. Many existing approaches also demand high computational resources, limiting real-time deployment on resource-constrained devices. To overcome these issues, the present work employs the lightweight MobileNet deep learning architecture for violence detection in surveillance videos. MobileNet is well-suited for embedded devices such as Raspberry Pi and Jetson Nano while maintaining competitive accuracy. In Python-based simulations on the Hockey Fight dataset, MobileNet is compared with AlexNet, VGG-16, and GoogleNet. Results show that MobileNet achieved 96.66% accuracy with a loss of 0.1329, outperforming the other models in both accuracy and efficiency. These findings demonstrate MobileNet’s superior balance of precision, computational cost, and real-time feasibility, offering a robust framework for intelligent surveillance in public safety monitoring, crowd management, and anomaly detection.
SN  - 3068-6679
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Hussain2025Detection,
  author = {Altaf Hussain},
  title = {Detection and Recognition of Real-Time Violence and Human Actions Recognition in Surveillance using Lightweight MobileNet Model},
  journal = {ICCK Journal of Image Analysis and Processing},
  year = {2025},
  volume = {1},
  number = {3},
  pages = {125-146},
  doi = {10.62762/JIAP.2025.839123},
  url = {https://www.icck.org/article/abs/JIAP.2025.839123},
  abstract = {Real-time detection of violent behavior through surveillance technologies is increasingly important for public safety. This study tackles the challenge of automatically distinguishing violent from non-violent activities in continuous video streams. Traditional surveillance depends on human monitoring, which is time-consuming and error-prone, highlighting the need for intelligent systems that detect abnormal behaviors accurately with low computational cost. A key difficulty lies in the ambiguity of defining violent actions and the reliance on large annotated datasets, which are costly to produce. Many existing approaches also demand high computational resources, limiting real-time deployment on resource-constrained devices. To overcome these issues, the present work employs the lightweight MobileNet deep learning architecture for violence detection in surveillance videos. MobileNet is well-suited for embedded devices such as Raspberry Pi and Jetson Nano while maintaining competitive accuracy. In Python-based simulations on the Hockey Fight dataset, MobileNet is compared with AlexNet, VGG-16, and GoogleNet. Results show that MobileNet achieved 96.66\% accuracy with a loss of 0.1329, outperforming the other models in both accuracy and efficiency. These findings demonstrate MobileNet’s superior balance of precision, computational cost, and real-time feasibility, offering a robust framework for intelligent surveillance in public safety monitoring, crowd management, and anomaly detection.},
  keywords = {real-time violence detection, CCTV surveillance video, convolutional neural networks, VGG-16, GoogLeNet, AlexNet, MobileNet},
  issn = {3068-6679},
  publisher = {Institute of Central Computation and Knowledge}
}