Conduent Business Services, LLC

Xerox Corporation

Hao Wu

Robert P Loce

Wencheng Wu

Beilei Xu

Yao Rong Wang

Graham Stephen Pennington

Howard A Mizes

Rakesh Suresh Kulkarni

Waqas Sultani

Zhigang Fan

Thomas F Wade

Mary Ann Sprague

Patricia Swenton-Wall

Eric Harte

Megan Clar

Faezeh Tafazzoli

A system for estimating parking occupancy includes a vehicle-detection device including an adjustable mast supporting an image capture device at a select height. The image capture device acquires video of a current parking area. A computer processor in communication with the image capture device is configured to receive the video data and define a region of interest in the video data. The processor is further configured to perform a spatial transform on the ROI to transform the ROI to a normalized geometric space. The processor is further configured to apply features of a detected object in the normalized geometric space to a vehicle classifier&#x2014;previously trained with samples acquired from a normalized camera perspective similar to the normalized geometric space&#x2014;and determine the occupancy of the current parking area using an output of the classifier.

Vision-based on-street parked vehicle detection via normalized-view classifiers and temporal filtering

Systems, methods, apparatus, and computer-implemented methods are provided for determining a camera calibration. The method includes accessing video data for a geographic area captured by a video camera. The video data is processed to detect a plurality of moving vehicles in the video data. A detection area is defined based on the plurality of moving vehicles. A plurality of line segments are identified for each of the plurality of vehicles detected in the video data. A plurality of vanishing points are estimated based on at least a subset of the plurality of line segments. A camera calibration is determined based on the plurality of vanishing points.

Camera calibration based on moving vehicle line segments

A method for gait analysis of a subject performed periodically over time to detect changes in one or more gait characteristics. The method includes detecting and identifying a subject and analyzing the gait of the subject on a plurality of occasions. Analyzing the gait of the subject includes, a detecting body parts, generating a joint model depicting the location of the at least one joint in each of the at least two frames, using the joint model to segment a gait cycle for the at least one joint, and comparing the gait cycle to a threshold value to detect abnormal gait.

Computer vision system for ambient long-term gait assessment

A computer-implemented system and method for gait analysis of a subject. The method includes obtaining visual data from an image capture device positioned in front of or behind the subject, the plurality of image frames capturing at least one gait cycle of the gait of the subject, detecting one or more prescribed features within the plurality of image frames, analyzing each of the plurality of image frames to detect cross-frame stability of the one or more prescribed features, and segmenting the gait of the subject into at least one gait cycle based at least in part on the detected cross-frame stability of the one or more prescribed features.

System and method for automatic gait cycle segmentation

A method, system, and apparatus for parking occupancy detection comprises collecting video of a blockface with at least one video recording module, identifying a number of possible parking spaces along the blockface in the collected video, defining, a region of interest for each of the possible parking spaces, detecting a time dependent occupancy of the defined regions of interest for each of the possible parking spaces, and reporting the time dependent occupancy. Drift correction of the recorded video and ground truth comparisons of occupancy determinations may be provided.

Mobile on-street parking occupancy detection

A system and method for creating a spatiotemporal image representation with information content that enables accurate and rapid processing for several applications.

System and method for spatiotemporal image fusion and integration

A method, system, and apparatus for video frame alignment comprises collecting video data comprising at least two video frames; extracting a line profile along at least one line profile in each of the at least two video frames; selecting one of the at least two video frames as a reference video frame; segmenting each of the at least one line profiles into a plurality of segmented line profile group segments; aligning the plurality of segmented line profiles with the segmented line profiles in the reference video frame; translating each of the at least two video frames for each of the plurality of corresponding segmented line profile alignments; and removing a camera shift from the at least two video frames according to the translation and alignment of the plurality of segmented line profiles with the plurality of segmented line profile in the reference video frame.

Methods and systems for camera drift correction

A system and method for assessing patient movement for Parkinson's disease includes capturing a video of a subject performing a finger tapping sequence comprising a predetermined number of open and close periods. According to an exemplary embodiment, a system and method includes extracting a region of interest for each frame of the video and generating a projection of the region of interest for each frame of the video using perpendicular vector projections in a direction or plurality of directions.

Automatic visual remote assessment of movement symptoms in people with parkinson's disease for MDS-UPDRS finger tapping task

A spatiotemporal system and method for parking occupancy detection. The system can generally include suitable image acquisition, processing, transmission and data storage devices configured to carry out the method which includes generating and processing spatiotemporal images to detect the presence of an object in a region of interest, such as a vehicle in a parking stall.

Video-based system and method for parking occupancy detection

A method and system for determining one or more dimension estimations for a vehicle. A sequence of image frames of a vehicle is received, and a digital representation of the vehicle is extracted from each image in the sequence of image frames. A bounding box is determined for the vehicle based upon the extracted digital representation for each digital representation of the vehicle and at least one dimension of the vehicle is estimated based upon the determined bounding box. An indication of the at least one estimated dimension is output. The indication of at least one estimated dimension is transferred as an input to a comparison technique for further processing the indication of the at least one estimated dimension. The comparison technique can include a parking determination process configured to determine a recommended spot for the vehicle based upon the indication of the at least one estimated dimension.

Vehicle dimension estimation from vehicle images

A method for detecting settle-down time in a space includes acquiring a sequence of frames capturing a select space from a first camera. The method includes determining an initial time for computing a duration it takes for an associated occupant to settle into a seat in the select space. The method includes determining one or more candidate frames from the sequence of frames where one or both of a sitting behavior and seat occupancy is observed at the seat. The method includes determining a final frame and a final time associated with the final frame from the one or more candidate frames. The method includes computing the settle-down time using the initial and the final times.

System and method for detecting settle down time using computer vision techniques

A method for detecting sitting behavior includes acquiring a sequence of frames capturing a scene-of-interest at an overhead view. The method includes detecting at least one empty seat within the scene-of-interest and associating the seat as being unoccupied and the frame as a reference frame. The method includes extracting reference features describing a region of the unoccupied seat in the reference frame and quantifying the reference features to form a reference feature vector. The method includes extracting features describing the region in a given frame and quantifying the features to form a current feature vector. The method includes measuring a change in a feature vector over time using the reference feature vector and the current feature vector. The method includes and determining a status of the seat in the given frame as being one of occupied and unoccupied based on the change in the feature vector.

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Pittsford, NY, United States of America

Hao Wu