Abstract:
Crowd anomaly detection is an essential aspect in computer vision applications, such as
public security monitoring and surveillance in crowded scenes. It is generally not feasible to
monitor manually, and consequently, the demand for automatic real-time systems has
emerged. Individual and hybrid deep learning approaches, such as Convolutional
Autoencoders (AE), Generative Adversarial Networks (GAN), as well as YOLOv8 are
presently explored. Although YOLOv8 is not the latest iteration in the series of YOLOs, it
remains worth due to its excellent balance between accuracy, speed, and the ability to support
several tasks of computer vision at once (detection, segmentation, classification, etc.). Its
simple-to-use ecosystem with full documentation and a small API enables it to be used in
many applications. Even if newer releases may include improvements in specific areas like
parameters or accuracy, YOLOv8 provides a solid, multi-tasking, well-supported solution
that is easier to use for most scenarios. On the other hand, AEs are good at recovering the
motion patterns, and GANs can achieve anomaly scoring, while YOLOv8 has a more
accurate object-level detection. However, none of them have satisfactory performance in
complex events. To tackle this issue, a hybrid framework comprising the three models was
proposed in this work using decision-level fusion to raise accuracy and reduce false
positives. Experimental results on UCSD Ped2 and UMN datasets demonstrate that the
proposed hybrid model performed better than single models in terms of precision, recall, F1-
score, and AUC. The proposed approach provides a scalable, robust, and real-time solution
for a cognitive surveillance system.
