Facial Image Analysis
Facial recognition algorithms use deep neural network models helping smart devices to identify user, understand users’ characteristics, estimate users’ emotions, etc.
Support 1:1 face verification and 1:N face recognition, white/black list management
Face Detection and Tracking
The system supports detecting and tracking human faces at real-time and is robust to rotation variations and occlusions.
Based on facial images, estimates attributes including age, gender, race. fast and accurate.
The system supports both active and non-intrusive methods for liveness detection.
Facial Image Clustering
Fast facial image clustering with locally computation. Suited for smartphone album management.
Detects human from video streams, Understands human behavior and recognize human characteristics, Enable smart devices sensing the human presence and conducting tasks such as “human following”
Human Presence Detect
Detect human presence in video stream with high accuracy
detects and localize human with various poses in video stream
Tracks human movement. Can be resumed after discontinued by interrupts.
Human Movement Trajectories
Detects human movement trajectories accurately in real-time. Supports both single and multiple person scenarios.
Hand Gesture Detection
Detects and recognize hands. Enable smart devices understand static hand posture and dynamic gestures.
Detects obstacles using vision techniques, exploring an unknown environment and navigates. Enable smart devices navigates autonomously without users’ guidance
Vision Based Obstacle Avoidance
Detects floor plane and obstacles, navigates autonomously. Help smart devices to sense obstacles more accurately.
Camera Positioning and Trajectory Tracking
Vision-based SLAM, Detects and tracks features, Enable smart devices self-localization in unknown environments
Combine 3D vision and deep learning, Estimate floor surface direction and object posture, Enable better understanding of the 3D space