Verkada Inc.

Zoox, Inc.

Yuanyuan Chen

Yunchao Gong

Yi Xu

Mayank Gupta

Naresh Nagabushan

Xia Yang

Zixiao (Shawn) Wang

Qiang (Kevin) Fu

Zeng Wang

James William Vaisey Philbin

David Pfeiffer

Janek Hudecek

Subhasis Das

Mark Jonathon McClelland

Song Cao

Dhanushka Nirmevan Kularatne

Suraj Arun Vathsa

Troy Donovan O′Neal

A vehicle can include various sensors to detect objects in an environment. In some cases, the object may be within a planned path of travel of the vehicle. In these cases, leaving the planned path may be dangerous to the passengers so the vehicle may, based on dimensions of the object, dimensions of the vehicle, and semantic information of the object, determine operational parameters associate with passing the object while maintaining a position within the planned path, if possible.

In-path obstacle detection and avoidance system

A computer vision processor of a camera generates hyperzooms for persons or vehicles from image frames captured by the camera. The hyperzooms include a first hyperzoom associated with the persons or vehicles. The computer vision processor tracks traffic patterns of the persons or vehicles while obviating network usage by the camera by predicting positions of the persons or vehicles using a Kalman Filter from the first hyperzoom. The persons or vehicles are detected in the second hyperzoom. The positions of the persons or vehicles are updated based on detecting the persons or vehicles in the second hyperzoom. The first hyperzoom is removed from the camera. Tracks of the persons or vehicles are generated based on the updated positions. The second hyperzoom is removed from the camera. Track metadata is generated from the tracks for storing in a key-value database located on a non-transitory computer-readable storage medium of the camera.

People and vehicle analytics on the edge

A computer vision processor of a camera extracts attributes of persons or vehicles from hyperzooms generated from image frames. The hyperzooms represent traffic patterns. The extracting is performed using a feature extractor of an on-camera convolutional neural network (CNN) including an inverted residual structure. The attributes include at least colors of clothing of the persons or colors of the vehicles. Mobile semantic segmentation models of the CNN are generated using the hyperzooms and the attributes. Attribute analytics are generated by executing the mobile semantic segmentation models while obviating network usage by the camera. The attribute analytics are stored in a key-value database located on a memory card of the camera. A query is received from the server instance specifying one or more of the attributes. The attribute analytics are filtered using the one or more of the attributes to obtain a portion of the traffic patterns.

Hyperzoom attribute analytics on the edge

Techniques for collision avoidance using an object contour are discussed. A trajectory associated with a vehicle may be received. Sensor data can be received from a sensor associated with the vehicle. A bounding contour may be determined and associated with an object represented in the sensor data. Based on the trajectory, a simulated position of the vehicle can be determined. Additionally, a predicted position of the bounding contour can be determined. Based on the simulated position of the vehicle and the predicted position of the bounding contour, a distance between the vehicle and the object may be determined. An action can be performed based on the distance between the vehicle and the object.

Collision avoidance using an object contour

A method and related system operations include obtaining a video stream with an image sensor of a camera device, detecting a plurality of target objects by executing a neural network model based on the video stream with a vision processor unit of the camera device. The method also includes generating a plurality of bounding boxes, determining a plurality of character sequences by, for each respective bounding box of the plurality of bounding boxes, performing a set of optical character recognition (OCR) operations to determine a respective character sequence of the plurality of character sequences. The method also includes updating a plurality of tracklets to indicate the plurality of bounding boxes and storing the plurality of tracklets in association with the plurality of character sequences in a memory of the camera device.

Automatic multi-plate recognition

Techniques for determining an object contour are discussed. Depth data associated with an object may be received. The depth data, such as lidar data, can be projected onto a two-dimensional plane. A first convex hull may be determined based on the projected lidar data. The first convex hull may include a plurality of boundary edges. A longest boundary edge, having a first endpoint and a second endpoint, can be determined. An angle can be determined based on the first endpoint, the second endpoint, and an interior point in the interior of the first convex hull. The longest boundary edge may be replaced with a first segment based on the first endpoint and the interior point, and a second segment based on the interior point and the second endpoint. An updated convex hull can be determined based on the first segment and the second segment.

Object contour determination

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San Mateo, CA, United States of America

Yuanyuan Chen