Snap Inc.

Verint Systems, Inc.

Verint Americas Inc.

Mobli Technologies 2010 Ltd.

Gadi Zimerman

Ido Sadeh

Oren Golan

Zvi Figov

Amir Sole

Shmuel Akerman

Doron Yankovitch

Ofer Waisman

Elad Falckovitch

Shmuel Kiro

Shahar Daliyot

Itsik Horovitz

Itshak Horovitz

A method of obtaining media content of an event, comprising: identifying a real-life event and, a time of said real-life event a geographic location of the real-life event; identifying a subset of a plurality of client terminals of users located in proximity to the geographic location of the real-life event at said time of said real-life event; sending a message to the subset of client terminals containing a request to acquire media content documenting the real-life event; and receiving at least one media content item documenting the real-life event from at least one client terminal of the subset of client terminals, the at least one media content item acquired by at least one user of the users using the at least one client terminal in response to the message.

Content request by location

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.

Time-in-store estimation using facial recognition

Models are generated from objects identified in video. Each model is evaluated based on knowledge of the objects determined from video analysis, and preferred models are identified based on the evaluations. In some examples, each model could be evaluated by tracking a movement of each object in the video by using each model to track the object from which it was generated, evaluating an ability of each model to identify the objects in the video that are similar to the object from which it was generated, and determining an amount of false identifications made by each model of different objects in different video that does not include the object from which it was generated.

Evaluation of models generated from objects in video

Embodiments disclosed herein provide systems and methods for matching trajectories across disjointed video views. In a particular embodiment, a method provides receiving a plurality of tagged trajectories that are tagged with an indicator of which trajectory pairs of the plurality of tagged trajectories are matching trajectory pairs and processing each of the trajectory pairs using each of a plurality of trajectory matching algorithms. The method further provides creating a model for matching trajectories based on the output of each of the plurality of trajectory matching algorithms for each of the pairs. The method further provides receiving a pair of trajectories and processing the pair of trajectories using each of the plurality of the trajectory matching algorithms. The method further provides processing the output of each of the plurality of the trajectory matching algorithms for the pair of trajectories using the model to determine whether the pair of trajectories is matching.

Trajectory matching across disjointed video views

Time-in store estimation using facial recognition

Growing community of inventors

Hod-Hasharon, Israel

Gadi Zimerman