Verint Systems, Inc.

Microsoft Technology Licensing, LLC

Verint Americas Inc.

Cognyte Technologies Israel Ltd.

Zvi Figov

Oren Golan

Gadi Zimerman

Nir Naor

Yonit Hoffman

Mattan Serry

Ofer Waisman

Elad Falckovitch

Doron Yankovitch

Maayan Yedidia

Irit Ofer

Amir Sole

Shmuel Kiro

Itshak Horovitz

Yaron Ostrovsky-Berman

Alexander Steven Johnson

Eliyahu Strugo

Tom Hirshberg

Michael Ben-haym

Mordechai Kadosh

Michael Sutton

A tagging system gathers all events (tagged and untagged) generated by remote sensors at a location or facility over time. Based on the gathered events the tagging system uses machine learning to train a model to learn the sensor layout of a facility or location and the timing between the triggering of sensors. Once trained, the model can predict the movement and location of individuals and objects throughout the facility based on a starting tagged event. Given a series of tagged and untagged events, the system can use the movement predictions of the model to tag the untagged events in the series with the identification of an individual or object that triggered the generation of the untagged event.

System and method for tagging untagged events

Systems, methods, and a computer-readable medium are provided for matching textless elements to texted elements in video content. A video processing system including a textless matching system may divide a video into shots, identify shots having similar durations, identify sequences of shots having similar durations, and compare image content in representative frames of the sequences to determine whether the sequences match. When the sequences are determined to match, the sequences may be paired, wherein the first sequence may include shots with overlaid text and the second sequence may include textless version of corresponding texted shots included in the first sequence. In some examples, the video processing system may further replace the determined corresponding texted shots.

Textless material scene matching in videos

Disclosed is a method for automatically detecting an introduction/opening song within a multimedia file. The method includes designating sequential blocks of time in the multimedia file as scene(s) and detecting certain feature(s) associated with each scene. The extracted scene feature(s) may be analyzed and used to assign a probability to each scene that the scene is part of the introduction/opening song. The probabilities may be used to classify each scene as either correlating to or not correlating to, the introduction/opening song. The temporal location of the opening song may be saved as index data associated with the multimedia file.

Combining visual and audio insights to detect opening scenes in multimedia files

Examples of the present disclosure describe improved systems and methods for detecting keywords in audio content. In one example implementation, audio content is segmented into one or more audio segments. One or more text segments is generated, each text segment corresponding to each of the audio segments. For each text segment, one or more phrase candidate values is generated using a textual analysis, and one or more sentence embedding values is generated using a sentence embedding analysis. Next, an average sentence embedding value is calculated using the one or more sentence embedding values. Each of the one or more phrase candidate values is compared to the average sentence embedding value. Each phrase candidate value having a comparison value above a threshold value is labeled as representing a keyword.

Keyword detection for audio content

A video-processing technique uses machine-trained logic to detect and track people that appear in video information. The technique then ranks the prominence of these people in the video information, to produce ranking information. The prominence of a person reflects a level of importance of the person in the video information, corresponding to the capacity of the person to draw the attention of a viewer. For instance, the prominence of the person reflects, at least in part, an extent to which the person appears in the video information. The technique performs its ranking based on person-specific feature information. The technique produces each instance of person-specific feature information by accumulating features pertaining to a particular person. One or more application systems make use of the ranking information to control the presentation of the video information.

Detecting prominence of objects in video information

A video indexing system identifies groups of frames within a video frame sequence captured by a static camera during a same scene. Context metadata is generated for each frame in each group based on an analysis of fewer than all frames in the group. The frames are indexed in a database in association with the generated context metadata.

Metadata generation for video indexing

The present invention generates a compression invariant motion timeline for a video. For each second in the video, the method uses an identification procedure to determine a stored key frame size, a frame counter, and an accumulated size of frames. The method then calculates and stores a motion value for the second using the stored key frame size, the frame counter, and the accumulated size of frames. The motion values for each second may be used to construct a timeline for the video.

System and method for generating a compression invariant motion timeline

An approach using 3D algorithms to solve 2D head localization problems is disclosed. A system can extrapolate aspects of one part of an object, e.g., extract characteristics of a person's head, using a 2D input image of another part of the object, e.g., a 2D image of the person's face. The system then selects an appropriate 3D model by the use of facial features detected in an image of a person's face. Using the selected 3D model and the 3D rotation angles provided by a face detector, the system rotates the model and then projects the model to a 2D shape. The system then scales and translates, e.g., transforms, the 2D shape to match the 2D face bounding box. Then, using the transformed 2D shape, the system extracts a bounding box for the extracted portion of an object, e.g., the head of the person depicted in the 2D input image.

Head position extrapolation based on a 3D model and image data

Tracking of targets in video captured by a multi-camera surveillance system is often difficult due to incomplete camera coverage and due to the complexities associated with automated recognition in a dense and highly variable environment. The present disclosure embraces a system and method for target tracking in a multi-camera surveillance system that reduces the time required for a user to track a target by offering computer generated search results that include suggestions of candidates found in the video that match (to some degree) the target. When one of the candidates is selected by a user, the search is shifted to other cameras and continued. In this way, the target may be tracked quickly. Further, a playlist of video snippets of the target is accumulated during the search and can be played to show all captured video of the target moving about the facility.

Target tracking in a multi-camera surveillance system

A system and method to determine a flow direction automatically so that counter-flow motion may be detected without user calibration is disclosed. The system and method utilize information available from existing video compression algorithms to sense motion and determine a flow direction. Once a flow direction for an area is determined, the system and method can detect counter-flow motion using compressed video without any decompression, thereby improving the speed of detection and simplifying the computational complexity. These improvements facilitate counter-flow alarms and expand the information available to a user monitoring the video.

Counter-flow detection in compressed video

A method of monitoring the amount of time spent in a specified area by an individual comprises employing a first camera to automatically create one or more entrance images, each entrance image containing a face of an entering individual that passes a first location, and storing each entrance image in a database along with a corresponding entrance time that the entering individual passed the entrance location. An exit image is compared to the entrance images in the database to identify a matching entrance image containing the same face as the exit image. A stay time is then determined for the exiting individual by determining the difference between the entrance time corresponding to the matching entrance image and the exit time.

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Modiin, Israel

Zvi Figov