An Optimization Approach for Action Understanding in Complex Social Scenes
A hierarchical processing strategy is recommended for action understanding in multi-person interaction scenarios:
- Scene Segmentation Technology: first extract the video keyframes with OpenCV (at 0.5 second intervals), get the individual bounding boxes with -instruct "Segment all visible persons", and then analyze each ROI individually
- Dynamic Branching EnhancementAdd the -branch_weight parameter to manually assign three branch weights (default 0.3:0.4:0.3), for example, 0.2:0.3:0.5 for interaction scenarios, example: python inference.py -modal video -branch_weight 0.2 0.3 0.5 -instruct "Analyze group interaction patterns"
- Timing Modeling Enhancements：对于超过30秒的长视频，建议先使用FFmpeg分段处理：ffmpeg -i input.mp4 -c copy -segment_time 00:00:30 -f segment output_%03d.mp4
- semantic enhancement cue: Specify elements of the scenario in the instructions, e.g. "Describe actions considering they are in a business meeting context"
Measurements show that this solution can increase the accuracy of interactive action recognition in conference room scenes from 68% to 82%. For scenes with more than 5 people, it is recommended to use an NVIDIA A100 graphics card to ensure real-time performance.
This answer comes from the articleHumanOmni: a multimodal macromodel for analyzing human video emotions and actionsThe
May not be reproduced without permission:AI productivity tools " How to optimize HumanOmni's action understanding in complex social scenarios?
