Detecting violent actions in extremely low-resolution (eLR) surveillance footage is challenging due to severe pixel degradation and motion ambiguity. This paper presents GAN-PPP, an integrated framework combining YOLOv8-based eLR object detection, domain-adapted SRGANs, and Mixture-of-Experts (MoE) LSTM modeling for enhanced violence recognition. The system detects action-relevant regions at 12
16 resolution, applies specialized SRGANs to recover fine pedestrian and pose details, and analyzes the restored sequences via MoE-LSTM to capture critical temporal dynamics. Experiments on HMDB51, UCF101, KTH, UCSD, Avenue, and Collective Activity (in eLR form) show that GAN-PPP improves accuracy by 11.4% over baseline eLR methods and surpasses C3D and LRCN by 9.2% and 7.8% on HMDB51-eLR. On UCF101-eLR, it achieves a 6.7% F1-score gain, confirming the value of super-resolution and expert-guided temporal fusion. GAN-PPP provides an effective ROI-based eLR violence-recognition framework and demonstrates practical inference speed on RTX 3090 hardware; however, deployment-level real-time performance under heterogeneous edge and surveillance conditions requires further validation.