Tracking after objects

• US 20200134329A1
• Filed: 08/20/2019
• Published: 04/30/2020
• Est. Priority Date: 10/26/2018
• Status: Active Grant

• Current Assignee: Autobrains Technologies Ltd.
• Original Assignee: Cartica Al Ltd.
• Inventors: Raichelgauz, Igal, Odinaev, Karina
• Granted Patent: US 11,126,869 B2
• Time in Patent Office: Days
• CPC Class Codes: G05D 1/0061 for transition from automatic pilot to manual pilot and vice versa
• CPC Class Codes: G05D 1/0088 characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours using knowledge based models G06N5/00
• CPC Class Codes: G05D 1/0094 involving pointing a payload, e.g. camera, weapon, sensor, towards a fixed or moving target
• CPC Class Codes: G05D 1/0238 using obstacle or wall sensors G05D1/0246 and G05D1/0289 take precedence; lidar systems designed for anti-collision purposes G01S17/93
• CPC Class Codes: G05D 1/0246 using a video camera in combination with image processing means
• CPC Class Codes: G05D 1/0253 extracting relative motion information from a plurality of images taken successively, e.g. visual odometry, optical flow determining position or orientation from images G06T7/70
• CPC Class Codes: G05D 1/0276 using signals provided by a source external to the vehicle involving a plurality of vehicles G05D1/0287; automatically controlling vehicle speed responsive to externally generated signals B60K31/0058
• CPC Class Codes: G05D 1/0287 involving a plurality of land vehicles, e.g. fleet or convoy travelling traffic control systems for road vehicles G08G1/00, particularly anticollision systems G08G1/16
• CPC Class Codes: G06F 18/211 Selection of the most significant subset of features
• CPC Class Codes: G06F 18/214 Generating training patterns; Bootstrap methods, e.g. bagging or boosting
• CPC Class Codes: G06F 18/23 Clustering techniques
• CPC Class Codes: G06F 18/25 Fusion techniques
• CPC Class Codes: G06F 18/28 Determining representative reference patterns, e.g. by averaging or distorting; Generating dictionaries
• CPC Class Codes: G06N 3/04 Architecture, e.g. interconnection topology
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 2207/20076 Probabilistic image processing
• CPC Class Codes: G06T 2207/20081 Training; Learning
• CPC Class Codes: G06T 2207/20084 Artificial neural networks [ANN]
• CPC Class Codes: G06T 2207/30252 : Vehicle exterior; Vicinity of vehicle
• CPC Class Codes: G06T 2207/30261 : Obstacle
• CPC Class Codes: G06T 7/246 : using feature-based methods, e.g. the tracking of corners or segments
• CPC Class Codes: G06T 7/70 : Determining position or orientation of objects or cameras camera calibration G06T7/80
• CPC Class Codes: G06T 7/74 : involving reference images or patches
• CPC Class Codes: G06V 10/255 : Detecting or recognising potential candidate objects based on visual cues, e.g. shapes
• CPC Class Codes: G06V 10/762 : using clustering, e.g. of similar faces in social networks
• CPC Class Codes: G06V 10/764 : using classification, e.g. of video objects
• CPC Class Codes: G06V 10/82 : using neural networks
• CPC Class Codes: G06V 20/56 : exterior to a vehicle by using sensors mounted on the vehicle
• CPC Class Codes: G06V 20/58 : Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
• CPC Class Codes: G06V 2201/08 : Detecting or categorising vehicles
• CPC Class Codes: G06V 40/103 : Static body considered as a whole, e.g. static pedestrian or occupant recognition
• CPC Class Codes: G08G 1/162 : event-triggered
• CPC Class Codes: G08G 1/165 : for passive traffic, e.g. including static obstacles, trees
• CPC Class Codes: G08G 1/166 : for active traffic, e.g. moving vehicles, pedestrians, bikes
• CPC Class Codes: H04L 67/12 : specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04N 7/183 : for receiving images from a single remote source
• CPC Class Codes: H04N 7/185 : from a mobile camera, e.g. for remote control
• CPC Class Codes: H04W 4/46 : for vehicle-to-vehicle communication [V2V]
• CPC Class Codes: H04W 4/48 : for in-vehicle communication

Abstract

A method for tracking after an entity, the method may include tracking, by a monitor of a vehicle, a movement of an entity that appears in various images acquired during a tracking period; generating, by a processing circuitry of the vehicle, an entity movement function that represents the movement of the entity during the tracking period; generating, by the processing circuitry of the vehicle, a compressed representation of the entity movement function; and responding to the compressed representation of the entity movement function

Claims

1. A method for tracking after an entity, the method comprises:

tracking, by a monitor of a vehicle, a movement of an entity that appears in various images acquired during a tracking period;

generating, by a processing circuitry of the vehicle, an entity movement function that represents the movement of the entity during the tracking period;

generating, by the processing circuitry of the vehicle, a compressed representation of the entity movement function; and

responding to the compressed representation of the entity movement function.

2. The method according to claim 1 wherein the compressed representation of the entity movement function is indicative of multiple properties of extremum points of the entity movement function.

3. The method according to claim 2 wherein multiple properties of an extremum point of the extremum points, comprise a location of the extremum point, and at least one derivative of the extremum point.

4. The method according to claim 2 wherein multiple properties of an extremum point of the extremum points, comprise a location of the extremum point, and at least two derivative of at least two different orders of the extremum point.

5. The method according to claim 2 wherein multiple properties of an extremum point of the extremum points, comprise a curvature of the function at a vicinity of the extremum point.

6. The method according to claim 2 wherein multiple properties of an extremum point of the extremum points, comprise a location and a curvature of the function at a vicinity of the extremum point.

7. The method according to claim 2 comprising acquiring the images by a visual sensor of the vehicle.

8. The method according to claim 2 wherein at least one image of the various images is acquired by an image sensor of another vehicle.

9. The method according to claim 2 wherein the responding comprises storing, in a memory unit of the vehicle, the compressed representation of the entity movement function.

10. The method according to claim 2 wherein the responding comprises transmitting the compressed representation of the entity movement function to a system that is located outside the vehicle.

11. The method according to claim 2 wherein the responding comprises estimating, by a processing circuitry of the vehicle, a future movement of the entity, based on the compressed representation of the entity movement function.

12. The method according to claim 2 wherein the responding comprises generating a profile of the entity, by a processing circuitry of the vehicle, based on the compressed representation of the entity movement function.

13. The method according to claim 2 wherein the responding comprises predicting an affect of a future movement of the entity on a future movement of the vehicle, wherein the predicting is executed by a processing circuitry of the vehicle, and is based on the compressed representation of the entity movement function.

14. The method according to claim 2 wherein the responding comprises searching for a certain movement pattern within the movement of the entity, by a processing circuitry of the vehicle, based on the compressed representation of the entity movement function.

15. The method according to claim 2 comprising receiving a compressed representation of another entity movement function, the other entity movement function is generated by another vehicle and is indicative of the movement of the entity during at least a subperiod of the tracking period.

16. The method according to claim 15 comprising amending the compressed representation of the entity movement function based on the compressed representation of the other entity movement function.

17. The method according to claim 2 comprising determining a duration of the tracking period.

18. A computerized system that comprises a monitor of a vehicle, a processing circuitry of the vehicle that are configured to track a movement of an entity that appears in various images acquired during a tracking period;

generate an entity movement function that represents the movement of the entity during the tracking period;

generate a compressed representation of the entity movement function; and

respond to the compressed representation of the entity movement function.

19. A non-transitory computer readable medium that stores instructions for:

tracking, by a monitor of a vehicle, a movement of an entity that appears in various images acquired during a tracking period;

generating, by a processing circuitry of the vehicle, an entity movement function that represents the movement of the entity during the tracking period;

generating, by the processing circuitry of the vehicle, a compressed representation of the entity movement function; and

responding to the compressed representation of the entity movement function.