## 4 Discussion
### Comparative Analysis with Recent Research in Violence and Pose Detection
[PERSON] et al. [34] and [PERSON] et al. [36] concentrate on violence detection based on object identification only, but our multi-faceted system detects violent actions, improving classification accuracy for diverse violent behavior;
* **Real-Time Performance and Robustness**: Similar to [PERSON] et al. [33] and [PERSON] et al. [34], we emphasize real-time performance and deploy our model on embedded systems for rapid inference. The combination of YOLO for object detection and MediaPipe for pose estimation ensures that our system functions optimally under real-world scenarios, such as surveillance in complex environments. Our optimized model provides high performance with low latency; thus, it is usable in urgent response scenarios, such as security monitoring;
* **Generalization Across Multiple Environments**: Our approach is able to handle a wide variety of video environments and hostile postures, addressing a common issue reported in [PERSON] et al. [37]. Using pretrained models and carefully curating the dataset, we ensure that our model generalizes well across multiple environments, hence being insensitive to a range of scenarios even when it encounters novel environments during training. **Weaknesses and Areas for Development**
* **Real-World Deployment Complexity**: While our approach demonstrates real-time performance, it may still be constrained in highly cluttered or low-resolution video settings where object and pose detection may be impaired. Similar to the constraints defined by [PERSON] et al. [33] and [PERSON] et al. [34], our system must deal with realistic real-world scenarios where occlusions, low light, or high-speed motions may impact detection accuracy;
* **Limited Action Classification Granularity**: While our system is strong in discriminating violent from non-violent action, further discrimination among violent behavior subclasses (i.e., physical assault, verbal aggression, or self-injury) would add more generality to the model. Fine-tuning in this way could lead to more specific intervention in real-world circumstances compared to a more general violent action classification;
* **Dependence on Pretrained Models**: While pretrained models are convenient in terms of improving initial performance and training, they also carry limitations when it comes to processing very particular sets of data. Our reliance on such models could restrict the capacity of the system to learn novel behaviors or unexpected actions, much like the iterative learning model presented by [PERSON] et al. [37]. In this work, a new contribution to the field of violence and pose detection is presented by integrating object detection and action recognition into an end-to-end system that can be applied in real-time on edge devices. Although adopting some of the features of recent progress, e.g., those of [PERSON] et al. [34] and [PERSON] et al. [36], our system differs on the grounds of its focus on the recognition of violent acts through physical postures and gestures, offering a deeper contextual interpretation of aggression. Although there remain some problems with environment adaptability and action classification granularity, our system provides a scalable and robust solution for violence detection that can be used in many public safety scenarios.