## 4 Discussion
### Comparative Analysis with Recent Research in Violence and Pose Detection  
In this section, a contrast of the current work with recent advancements in the field of violence and pose detection is presented and highlighting the similarities, the differentiating aspects, strengths, and weaknesses with respect to current techniques. The papers of [PERSON] et al. [33], [PERSON] et al. [34], [PERSON] and [PERSON] [35], [PERSON] et al. [36], [PERSON] et al. [37], and [PERSON] et al. [38] that we considered provide valuable information on the state-of-the-art methods in violence detection and pose estimation. We compare and summarize below significant information from these studies with our approach. **Similarities and Common Approaches**  
* **Deep Models for Object and Action Detection**: Like [PERSON] et al. [34], our methodology also utilizes sophisticated deep models (YOLOv7 and LSTM) for object detection and action detection. Both methods seek to boost real-time detection accuracy for the recognition of violent behavior. Our emphasis, however, on recognizing a wider variety of violent postures, including defensive and aggressive movements, sets our work apart by enhancing the applications beyond violent objects only;
* **Real-Time Edge Deployment**: As is the case with [PERSON] et al. [33] and [PERSON] et al. [34], our solution gives emphasis to real-time edge deployment. The approach takes embedded system deployment complexities into account and uses low-latency-optimized YOLO and MediaPipe that ensures quick detection with instantaneous alerting within real setups. This priority entails that the developed system finds usability in safety-critical operations, as presented by both [PERSON] et al. [33] and [PERSON] et al. [34];
* **Action Recognition and Pose Estimation**: [PERSON] and [PERSON] [35] designed a real-time 3D pose recognition system from a deep convolutional neural network, with some resemblance to our pose estimation technique. Though [PERSON] and [PERSON] are concerned with character animation, we utilize a holistic model to estimate body poses, using MediaPipe to detect physical postures signaling violence. This intersection signifies a shared interest in applying pose analysis to action recognition systems, although our application is to real-world violence detection. **Differentiation and Unique Contributions**  
* **Focus on Violence Detection Based on Holistic Postures of the Body**: While most of the works reviewed, e.g., [PERSON] et al. [34] and [PERSON] et al. [36], focus on detecting violent objects (e.g., weapons), our work is novel in its focus on detecting violent actions through body postures and movement. Both offensive and defensive moves are covered under this work, which can provide insights into violence and aggression based on the motion of the human body rather than based on objects utilized;* **Enhanced Dataset and Action Classification**: In contrast to [PERSON] et al. [37], who utilize an active learning approach with a threshold that is used for continuously enhancing the classifier across environments, our approach includes a multimodal action classification system. While [PERSON] et al. [37] rely on recalibrating the model on new ambiguous images to produce higher accuracy, our solution improves on that idea by focusing on the physical postures of violence and classifying them as defensive and aggressive. This enables more subtle violent behaviors beyond simple object detection;
* **Multi-Class Pose Recognition**: Our system identifies poses and actions in a real setting, as opposed to [PERSON] and [PERSON] [35], who work on identifying animated character poses. Our system is specifically designed to identify violent and aggressive postures from video streams, which is an important practical distinction in deployment. **Strengths**
* **Inter-Model Comprehensive Integration**: Our approach combines object detection (via YOLOv7) and pose estimation (via MediaPipe) into a single solution, providing an end-to-end solution for both the object and the action context of violence.