Honeywell International Inc.

Koninklijke Philips Corporation N.V.

Activeye, Inc.

Tomas Brodsky

Yun-Ting Lin

Mi-Suen Lee

Srinivas Gutta

Eric Cohen-Solal

Carolyn Christine Ramsey

Yunting Lin

Miroslav Trajkovic

Aravind Padmanabhan

Hugo Strubbe

Vasanth Philomin

Damian M Lyons

Vit Libal

Serhan Dagtas

Peter J Janssen

Antonio Colmenarez

Michael D Pashley

Manjunatha Divakara

Robert A Cohen

Daphna Weinshall

Sudhindra D Mewundi

A facial recognition system includes a memory for storing a facial image database, wherein the facial image database includes a plurality of entries each corresponding to a different person, and wherein each entry includes a person identifier along with one or more facial images of the person. The facial recognition system further includes a facial recognition module that is operatively coupled to the memory. The facial recognition module is configured to receive a new facial image, and to select one or more facial recognition engines based on one or more facial image parameters of the new facial image, and to use the selected facial recognition engines to compare the new facial image with facial models that are based upon facial images stored in the facial image database in order to identify the person in the new facial image.

Facial enrollment and recognition system

A method of reducing false positives and identifying relevant true alerts in a video management system includes analyzing images to look for patterns indicating changes between subsequent images. When a pattern indicating changes between subsequent images is found, the video management system solicits from a user an indication of whether the pattern belongs to one of two or more predefined categories. The patterns indicating changes between subsequent images are saved for subsequent use. Subsequent images received from the video camera are analyzed to look for patterns indicating changes between subsequent images. When a pattern indicating changes between subsequent images is detected by the video management system, the video management system compares the pattern indicating changes between subsequent images to those previously categorized into one of the two or more predefined categories. Based on the comparison, the video management system may provide an alert to the user.

Systems and methods for detecting patterns within video content

The filtering tasks that are conventionally applied in a video monitoring application, to distinguish images that may be relevant to the application, are distributed to the image source, or near-source devices. Source devices, such as cameras and playback devices, and near-source devices, such as video concentrators and streaming devices, are configured to include video processing tools that can be used to pre-filter the image data to identify frames or segments of frames that include image information that is likely to be relevant to the receiving video monitoring application. In this manner, the receiving processor need not spend time and resources processing images that are pre-determined to be irrelevant to the receiving application.

Relevant image detection in a camera, recorder, or video streaming device

A data analysis system and approach having sensors, a collective processing mechanism connected to the sensors, and a threshold logic panel connected to the collective processing mechanism. Raw data from the sensors may be subject to collective processing and collective threshold logic analysis. The collective processing mechanism and the threshold logic panel may be situated outside of or in the cloud. Big data analytics may be performed on the data. The sensors may be homogeneous or heterogeneous. Consequently, there may be data fusion for false alarm reduction and advanced alarm detection, and application of big data analysis. Raw data may be used for determining positional information.

Sensor data processing system for various applications

A system and method of detecting tampering at an automatic teller machine includes detecting start and end indicators of a transaction. A representation of a scene at the teller machine, prior to the start of the transaction can be compared to a representation of the scene after the end of the transaction. Variations therebetween can indicate tampering at the machine.

System and method to detect tampering at ATM machines

Blocking zones of a surveillance system are configured to prevent false alarms caused by motion within the zones, while allowing tracking of objects through the zones. An object that first appears within a blocking zone is not considered to be a reportable object until the object leaves the zone. All reportable objects are tracked, without regard to the blocking zones. Objects that remain within their initial blocking zone are not deemed to be reportable objects, and therefore do not generate alarms. If an object initially appears within overlapping zones, the object is not deemed to be reportable until it leaves each of the zones at least once. The blocking zones do not mask the video images, and thus a complete record of activity is available for forensic purposes.

Object blocking zones to reduce false alarms in video surveillance systems

A video interface kernel with a defined application program interface includes each of a plurality of core functions for interfacing with video equipment and video processing subsystems. The core functions include such functions as video capture and video output, video recording and playback, and event notification. Functions of the video interface kernel also include video analysis functions and/or interfaces to analysis subsystems, as well as interfaces to point-of-sale terminals, access control systems, and location tracking systems. An intelligent video management module facilitates managing the interactions among the other modules of the kernel, further simplifying the application level interface.

Video system interface kernel

A stereo camera system including: a stereo imaging system such as two or more cameras or a camera and a set of angled mirrors, for outputting at least one stereo image; a recognition system for locating an object of interest in the field of view of the stereo imaging system and at least one of a distance of the object of interest from the stereo imaging system and the size of the object of interest; and an adjustor for automatically changing at least one system parameter which affects the spatial resolution of the object of interest based on at least one of the located distance of the object of interest from the stereo imaging system and the size of the object of interest.

Self adjusting stereo camera system

To calibrate images from an overhead camera, two equal-length reference lines at different heights relative to the floor plane are imaged. By comparing the resultant image-width of each of these lines in the view provided by the camera, the effective focal point of the camera is determined. In a preferred embodiment, a doorframe is used to provide equal-length lines at different heights parallel to the floor. The threshold of the doorway at the floor plane is used as a reference plane, and a parallel calibration line at a known/measured height in the doorway is defined. The image produced by a vertically oriented camera of these two equal length lines will show a longer line at the elevated position, the different widths of the images of these lines being dependent upon the camera's effective focal point. From these two projections of the equal-length lines at different heights, the camera's focal point distance from the reference floor plane is determined.

Size calibration and mapping in overhead camera view

A hand-held processing device, such as a mobile telephone, a personal digital assistant (PDA), or a portable computer, includes a physically or electronically adjustable camera and is operative to track a specified object of interest. Relative movement between the hand-held device and the object of interest is detected using an orientation determination device such as a gyroscope, an image-based tracking operation, or a hybrid combination of orientation determination and image-based tracking. A setting of the camera is then adjusted so as to maintain a desired framing of the object of interest within an image generated by the camera.

Method and apparatus for tracking an object of interest using a camera associated with a hand-held processing device

A method and system is configured to characterize regions of an environment by the likelihoods of transition of a target from each region to another. The likelihoods of transition between regions is preferably used in combination with conventional object-tracking algorithms to determine the likelihood that a newly-appearing object in a scene corresponds to a recently-disappeared target. The likelihoods of transition may be predefined based on the particular environment, or may be determined based on prior appearances and disappearances in the environment, or a combination of both. The likelihoods of transition may also vary as a function of the time of day, day of the week, and other factors that may affect the likelihoods of transitions between regions in the particular surveillance environment.

Linking tracked objects that undergo temporary occlusion

A vehicle identification system distinguishes vehicles in reduced visibility conditions based on the characteristic projection patterns associated with vehicle headlights and their reflections. Recognizing that the appearance of reflections of headlight beams in a camera image is highly dependent upon the angle of incidence/reflection relative to the camera, whereas the appearance of the headlights in the camera image is relatively consistent, the vehicle identification system distinguishes vehicles from their reflections based on the tracks of the illumination patterns in a series of images. In a first embodiment, headlights are distinguished from reflections based on a length of the tracks. In a second embodiment, recognition zones are selected as regions in the camera image that are not likely to provide reflections from normally aligned headlight beams. Pattern-matching and motion-correlation techniques are also used to identify the track of each vehicle in prior and subsequent image frames.

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Croton on Hudson, NY, United States of America

Tomas Brodsky