Altumview Systems Inc.

Telefonaktiebolaget Lm Ericsson (Publ)

Ericsson Ab

Minghua Chen

Xing Xing Wang

Jie Liang

Him Wai Ng

Yu Gao

Jianbing Wu

Mehdi Seyfi

Jiannan Zheng

Ye Lu

Rui Ma

Chao Shen

Desmond Yan

Eric Honsch

Thomas Wing Tong Ng

Weiming Wang

Blair Mackiewich

Andrew Tsun-Hong Au

Chi Chung Chan

Dong Zhang

Kaichao Wang

Adrian Kee-Ley Auk

Karen Ly-Ma

Adrian Fettes

Kuan Huan Lin

Kwun-Keat Chan

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 an age and gender estimation system capable of performing age and gender classifications on face images having sizes greater than the maximum number of input pixels supported by a given small-scale hardware convolutional neural network (CNN) module. In some embodiments, the proposed age and gender estimation system can first divide a high-resolution input face image into a set of image patches with judiciously designed overlaps among neighbouring patches. Each of the image patches can then be processed with a small-scale CNN module, such as the built-in CNN module in Hi3519 SoC. The outputs corresponding to the set of image patches can be subsequently merged to obtain the output corresponding to the input face image, and the merged output can be further processed by subsequent layers in the age and gender estimation system to generate age and gender classifications for the input face image.

Age and gender estimation using small-scale convolutional neural network (CNN) modules for embedded systems

Embodiments described herein provide various examples of a real-time face-detection, face-tracking, and face-pose-selection subsystem within an embedded video system. In one aspect, a process for performing real-time face-pose-estimation and best-pose selection for a detected person captured in a video is disclosed. This process includes the steps of: receiving a video image among a sequence of video frames of a video; performing a face detection operation on the video image to detect a set of faces in the video image; detecting a new person appears in the video based on the set of detected faces; tracking the new person through subsequent video images in the video by detecting a sequence of face images of the new person in the subsequent video images; and for each of the subsequent video images which contains a detected face of the new person being tracked: estimating a pose associated with the detected face and updating a best pose for the new person based on the estimated pose. Upon detecting that the new person has disappeared from the video, the process then transmits a detected face of the new person corresponding to the current best pose to a server, wherein transmitting the detected face having the best pose among the sequence of detected face images reduces network bandwidth and improves storage efficiency.

Method and apparatus for real-time face-tracking and face-pose-selection on embedded vision systems

Embodiments described herein provide various examples of a joint face-detection and head-pose-angle-estimation system based on using a small-scale hardware CNN module such as the built-in CNN module in HiSilicon Hi3519 system-on-chip. In some embodiments, the disclosed joint face-detection and head-pose-angle-estimation system is configured to jointly perform multiple tasks of detecting most or all faces in a sequence of video frames, generating pose-angle estimations for the detected faces, tracking detected faces of a same person across the sequence of video frames, and generating 'best-pose' estimation for the person being tracked. The disclosed joint face-detection and pose-angle-estimation system can be implemented on resource-limited embedded systems such as smart camera systems that are only integrated with one or more small-scale CNN modules. The proposed system in conjunction with using a subimage-based technique has made it possible to performance multiple face detection and face recognition tasks on high-resolution input images with small-scale low-cost CNN modules.

Joint face-detection and head-pose-angle-estimation using small-scale convolutional neural network (CNN) modules for embedded systems

Embodiments described herein provide various examples of a face detection system, based on using a small-scale hardware convolutional neural network (CNN) module configured into a multi-task cascaded CNN. In some embodiments, a subimage-based CNN system can be configured to be equivalent to a large-scale CNN that processes the entire input image without partitioning such that the output of the subimage-based CNN system can be exactly identical to the output of the large-scale CNN. Based on this observation, some embodiments of this patent disclosure make use of the subimage-based CNN system and technique on one or more stages of a cascaded CNN or a multitask cascaded CNN (MTCNN) so that a larger input image to a given stage of the cascaded CNN or the MTCNN can be partitioned into a set of subimages of a smaller size. As a result, each stage of the cascaded CNN or the MTCNN can use the same small-scale hardware CNN module that is associated with a maximum input image size constraint.

Face detection using small-scale convolutional neural network (CNN) modules for embedded systems

Apparatus and techniques for scheduling packets to reduce congestion in a packet data network are described. In one embodiment of the invention, packets are received through each of a plurality of channels and are stored in a respective queue for each channel. The status of each queue is monitored as empty or not empty. When an empty queue becomes not empty, then bandwidth allocation value is added for the respective channel to a port load value. When a not empty queue becomes empty, then the bandwidth allocation for the respective channel is subtracted from the port load value. The traffic rate through the port is then scaled based upon the port load value.

Scheduling under congestion with traffic load-based scaling

A method and apparatus for a traffic shaper that uses a traffic shaping algorithm based on a sustained rate token bucket and a constant rate emitter. The sustained rate token bucket uses a plurality of tokens based on the sustained rate and the sustained burst size. The constant rate emitter allows transmission of traffic at an adjusted transmission rate, where the adjusted transmission rate is derived from the peak rate, peak burst size, sustained rate and sustained burst size.

Method and apparatus for traffic shaping

A method and apparatus to shape packet traffic rates of packets in-transit from a plurality of channels to a port, monitor a status of each queue, subtract a CIR from a port load value when an empty queue becomes active, add the CIR to the port load value when an active queue becomes empty, add a difference between an EIR and the CIR for a channel to a port excess value when a queue of the channel becomes excess, subtract the difference for the channel from the port excess value when the excess queue for the channel becomes not excess, calculate an oversubscription ratio based on the port load value and port excess value, calculate a shaping rate for each of the plurality of channels, and shape the traffic for each of the plurality channels using the shaping rate.

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Coquitlam, Canada

Minghua Chen