Honeywell International Inc.

Gurumurthy Swaminathan

Kwong Wing Au

Saad J Bedros

Mahesh Kumar Gellaboina

Yadhunandan Us

Mohammed Ibrahim Mohideen

Ben Miller

Vijendran Gopalan Venkoparao

Jana Trojanova

Yadhunandan U S

Yadhunandan Ullam Subbaraya

Isaac D Cohen

Vinod Pathangay

Dinesh Ramegowda

Niranjan Rao

Pramod Nc

Chaitanya Krishna Paturu

Michal Juza

Rudra N Hota

Ullam Subbaraya Yadhunandan

Lokanatha Reddy J

Dhananjayan S

MaryRosaline Mariappan

Systems and methods for detecting motion in compressed video are provided. Some methods can include parsing a stream of compressed video, obtaining macroblock size information from the parsed stream, computing factors derived from the macroblock size information, computing adaptive threshold values derived from relative frame characteristics of the compressed video, comparing the factors derived from the macroblock size information with the adaptive threshold values, and detecting motion based upon the comparing when at least one of the factors exceeds at least one of the adaptive threshold values. In some embodiments, detecting the motion can include performing spatio-temporal filtering on macroblocks in which the motion is detected or performing spatio-temporal filtering on at least one non-motion macroblock.

System and method for detecting motion in compressed video

Systems and methods of motion detection on encrypted or scrambled video data streams are provided. Some methods can include identifying macroblock size information for an encrypted/scrambled video data stream and using the identified macroblock size information to determine a presence of motion in the encrypted/scrambled video data stream without decrypting and descrambling the encrypted/scrambled video data stream.

System and method of motion detection on encrypted or scrambled video data streams

A method and apparatus wherein the method includes the steps of parsing a stream of compressed video, obtaining macroblock size information from the parsed stream, computing factors derived from the macroblock size, wherein the factors include a normalized bit size, a bit size ratio and a neighbor score, computing corresponding adaptive threshold values derived from the relative frame characteristics of the compressed video, comparing the factors derived from the macroblock size information with the corresponding adaptive threshold values and detecting motion based upon combinations of the comparisons when the factors exceed the threshold value.

Devices, methods, and systems for determining pushback direction are described herein. One method includes receiving a video image of an aircraft during a pushback of the aircraft, determining a motion flow map associated with the video image during the pushback, determining a motion orientation histogram associated with the video image during the pushback, and determining a direction of the pushback based on the motion flow map and the motion orientation histogram.

Determining pushback direction

A method includes receiving an image of a face to match with images of known faces, extracting blocks multiple blocks from the received image, calculating local binary pattern histograms for each block, generating matching scores for each block against block of the images of known faces, determining a top number, N, of matching scores less than the number of blocks, and matching the received image to an image of a known face as a function of the top number of matching scores.

Face matching for mobile devices

A system and method include obtaining an image of an analog dial gauge. The image is processed to identify an endpoint of the gauge and a needle position in the image. A reading of the gauge is determined from the endpoint, the needle position, and information regarding the range of the gauge.

Image based dial gauge reading

Devices, methods, and systems for detecting motion in a high resolution video are described herein. One method includes dividing an image frame of a high resolution video into a number of blocks, determining whether motion is present in each of the blocks, and identifying, within each block in which motion is determined to be present, pixels in which motion is present.

Detecting motion in a high resolution video

The present disclosure provides methods, machine readable media, and systems for object alignment from a 2-dimensional (2-D) image of the object. One or more embodiments include defining a 2-D shape in the 2-D image and a 3-dimensional (3-D) shape in a 3-D model of the object, mapping a number of corresponding points on the 2-D and 3-D shapes, defining the 2-D and 3-D shapes with a number of triangles, wherein a number of vertices of the number of triangles correspond to the number of points, subdividing the number of triangles defining the 2-D and 3-D shapes into a plurality of subdivided triangles that include a plurality of new vertices, and reconstructuring a 3-D image from the 2-D image by assigning a number of z-coordinates from the plurality of subdivided triangles of the 3-D shape to the plurality of subdivided triangles of the 2-D shape to create a 3-D reconstructured shape.

Object alignment from a 2-dimensional image

Methods, devices, and systems for object tracking are described herein. One or more method embodiments include receiving an initial set of track points associated with a trajectory of an object, compressing the initial set of track points into a plurality of track segments, each track segment having a start track point and an end track point, and storing the plurality of track segments to represent the trajectory of the object.

Object tracking

A system and method track a face moving through multiple frames of a video sequence. A predicted position of a face in a video frame is obtained. A search is performed to determine appearance model similarity values of a detected face. Motion and appearance probabilities of the detected face are obtained, and the detected face in the video frame is associated with a track as a function of a joint probabilistic data association filter.

Multiple view face tracking

A method of operating an industrial plant having processing units that includes a wireless network including a network server, at least one router, and a plurality of workers including a first worker. A first handheld computing device is provided having a wireless transceiver and a processor programmed to implement a location-based service algorithm stored in non-transitory machine readable storage to a first worker. The location-based service algorithm implements obtaining location information that identifies a physical location for the first worker, and adding the location information to wireless transmissions of information by the first worker to others of the plurality of workers or to the network server. The first handheld computing device wirelessly receives server provided plant information that is a function of the physical location, and the first worker performs at least one action guided at least in part by the server provided plant information.

Handheld device having location-based features for plant workers

Systems and methods for detecting people in video data streams or image data are provided. The method includes using a plurality of training images for learning spatial distributions associated with a plurality of body parts, detecting a plurality of detections of body parts in an input image, clustering the detections of body parts located within a predetermined distance from one another to create one effective detection for each cluster of detections, and determining a position of each person associated with each effective detection. The detections of body parts can be associated with respective previously learned spatial distributions.

Detection of people in real world videos and images

A system and method are provided for tracking a face moving through multiple frames of a video sequence. A predicted position of a face in a video frame is obtained. Similarity matching for both a color model and an edge model are performed to derive correlation values for each about the predicted position. The correlation values are then combined to determine a best position and scale match to track a face in the video.

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Bengaluru, India

Gurumurthy Swaminathan