Investigating state-of-the-art vision transformers, hardware-aware quantization techniques, spatio-temporal multi-object tracking, and privacy-preserving video obfuscation.
![Genovaxai Real-Time Camera Feed - UAV-ALPHA [AIRFRAME & OPTICAL GIMBAL TRACKING]](/images/product-aerial.jpg "Genovaxai Optical Vision - UAV-ALPHA [AIRFRAME & OPTICAL GIMBAL TRACKING]")
Investigating layer-specific mixed INT8/FP16 quantization on Vision Transformer backbones to eliminate spatial attention latency without degrading fine-grained industrial defect classification.
Architecting temporal transition graphs that model probable transit velocities between disparate factory floor cameras to resolve long-interval identity re-acquisition under severe occlusions.
Eliminating host-to-device memory copies in RTSP H.264/H.265 video decoding pipelines by bonding hardware NVDEC buffers directly to unified unified memory pointers.
Developing reversible cryptographic bounding-box blurring executed at 60 FPS directly within the video ingestion layer to guarantee privacy compliance before storage.
Exploring post-training INT8 and INT4 quantization to execute 100M+ parameter vision models on sub-15W edge SoMs without perceptual degradation.
Developing lightweight graph convolutional networks for persistent multi-camera tracking across blind spots and visual obstacles.
Hardware-accelerated edge face and license plate blurring ensuring zero biometric data leaves the customer perimeter without consent.
