Altumview Systems Inc.

Yu Gao

Xing Xing Wang

Jie Liang

Minghua Chen

Ye Lu

Jianbing Wu

Rui Ma

Chao Shen

Him Wai Ng

Andrew Tsun-Hong Au

Chi Chung Chan

Dong Zhang

Eric Honsch

Jiannan Zheng

Karen Ly-Ma

Adrian Fettes

Zachary DeVries

Kuan Huan Lin

Kwun-Keat Chan

Adrian Kee-Ley Auk

The present disclosure provides a method and system for efficiently and securely managing human motion data. In one aspect, a human-motion-data-managing system first receives a sequence of video images including a first person. Next, for each image in the sequence of video images, the human-motion-data-managing system detects the first person in the video image; and subsequently extracts a skeleton figure of the detected first person from the image, wherein the skeleton figure is composed of a set of human keypoints. Next, human-motion-data-managing system combines a sequence of extracted skeleton figures of the detected first person from the sequence of video images to form a skeleton sequence of the detected first person which depicts a continuous motion of the first person. The human-motion-data-managing system subsequently transmits the skeleton sequence to a server in place of the actual images of the first person to preserve the privacy of the first person.

Privacy-preserving human action recognition, storage, and retrieval via joint edge and cloud computing

Embodiments described herein provide various examples of real-time visual object tracking. In another aspect, a process for performing a local re-identification of a target object which was earlier detected in a video but later lost when tracking the target object is disclosed. This process begins by receiving a current video frame of the video and a predicted location of the target object. The process then places a current search window in the current video frame centered on or in the vicinity of the predicted location of the target object. Next, the process extracts a feature map from an image patch within the current search window. The process further retrieves a set of stored feature maps computed at a set of previously-determined locations of the target object from a set of previously-processed video frames in the video. The process next computes a set of correlation maps between the feature map and each of the set of stored feature maps. The process then attempts to re-identify the target object locally in the current video frame based on the set of computed correlation maps.

Real-time visual object tracking for unmanned aerial vehicles (UAVs)

Embodiments described herein provide examples of a real-time visual object tracking system. In one aspect, an unmanned aerial vehicle (UAV) capable of performing real-time visual tracking of a moving object includes: a processor; a memory coupled to the processor; and a camera to capture a video of the moving object. This UAV additionally includes a visual tracking module to: receive a first video image and a first location of the object; receive a second video image following the first video image; place a first search window in the first video image and a second search window in the second video image centered on a second location in the second video image having the same coordinates as the first location; compute a correlation between an image patch within the first search window and an image patch within the second search window; and determine an updated location of the object in the second video image.

High-performance visual object tracking for embedded vision systems

Various embodiments of a vision-based privacy-preserving embedded fall-detection system are disclosed. This embedded fall-detection system can include one or more cameras for capturing video images of one or more persons. Moreover, this embedded fall-detection system can include various fall-detection modules for processing the captured video images including: a pose-estimation module, an action-recognition module, and a fall-detection module, all of which can perform the intended fall-detection functionalities within the embedded system environment in real-time in order to detect falls of the one or more persons. When a fall is detected, instead of sending the original captured images, the embedded fall-detection system can transmit sanitized video images to the server, wherein each detected person is represented by a skeleton figure in place of the actual person images, thereby preserving the privacy of the detected person. The embedded fall-detection system can be implemented as an embedded vision sensor to be installed at a single fixed location.

Method and system for privacy-preserving fall detection

Embodiments described herein provide various examples of an automatic obstacle avoidance system for unmanned vehicles using embedded stereo vision techniques. In one aspect, an unmanned aerial vehicle (UAV) capable of performing autonomous obstacle detection and avoidance is disclosed. This UAV includes: a stereo vision camera set coupled to the one or more processors and the memory to capture a sequence of stereo images; and a stereo vision module configured to: receive a pair of stereo images captured by a pair of stereo vision cameras; perform a border cropping operation on the pair of stereo images to obtain a pair of cropped stereo images; perform a sub sampling operation on the pair of cropped stereo images to obtain a pair of sub sampled stereo images; and perform a dense stereo matching operation on the pair of sub sampled stereo images to generate a dense three-dimensional (3D) point map of a space corresponding to the pair of stereo images.

Obstacle avoidance system based on embedded stereo vision for unmanned aerial vehicles

Embodiments described herein provide various examples of a real-time face-detection, face-tracking, and face-pose-selection subsystem within an embedded vision system. In one aspect, a process for identifying near-duplicate-face images using this subsystem is disclosed. This process includes the steps of: receiving a determined best-pose-face image associated with a tracked face when the tracked face is determined to be lost; extracting an image feature from the best-pose-face image; computing a set of similarity values between the extracted image feature and each of a set of stored image features in a feature buffer, wherein the set of stored image features are extracted from a set of previously transmitted best-pose-face images; determining if any of the computed similarity values is above a predetermined threshold; and if no computed similarity value is above the predetermined threshold, transmitting the best-pose-face image to a server and storing the extracted image feature into the feature buffer.

Enhanced face-detection and face-tracking for resource-limited embedded vision systems

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Vancouver, Canada

Yu Gao