Fotonation Limited

Tobii Technologies Limited

Dragos Dinu

Petronel Bigioi

Alexandru Caliman

Mihai Constantin Munteanu

Corneliu Zaharia

Szabolcs Fulop

Mihai Constantine Munteanu

Szaboles Fulop

Radu Ene

A method for monitoring occupants of a vehicle comprises identifying a respective body region for one or more occupants of the vehicle within an obtained image; identifying within the body regions, skeletal points including points on an arm of a body; identifying one or more hand regions; and determining a hand region to be either a left or a right hand of an occupant in accordance with its spatial relationship with identified skeletal points of the body region of an occupant. The left or right hand region for the occupant are provided to a pair of classifiers to provide an activity classification for the occupant, a first classifier being trained with images of hands of occupants in states where objects involved are not visible and a second classifier being trained with images of occupants in the states where the objects are visible in at least one hand region.

Vehicle occupant monitoring system and method

A method of tracking an object across a stream of images comprises determining a region of interest (ROI) bounding the object in an initial frame of an image stream. A HOG map is provided for the ROI by: dividing the ROI into an array of M×N cells, each cell comprising a plurality of image pixels; and determining a HOG for each of the cells. The HOG map is stored as indicative of the features of the object. Subsequent frames are acquired from the stream of images. The frames are scanned ROI by ROI to identify a candidate ROI having a HOG map best matching the stored HOG map features. If the match meets a threshold, the stored HOG map indicative of the features of the object is updated according to the HOG map for the best matching candidate ROI.

Method and system for tracking an object

A method of tracking an object across a stream of images comprises determining a region of interest (ROI) bounding the object in an initial frame of an image stream. A HOG map is provided for the ROI by: dividing the ROI into an array of M&#xd7;N cells, each cell comprising a plurality of image pixels; and determining a HOG for each of the cells. The HOG map is stored as indicative of the features of the object. Subsequent frames are acquired from the stream of images. The frames are scanned ROI by ROI to identify a candidate ROI having a HOG map best matching the stored HOG map features. If the match meets a threshold, the stored HOG map indicative of the features of the object is updated according to the HOG map for the best matching candidate ROI.

A convolutional neural network (CNN) for an image processing system comprises an image cache responsive to a request to read a block of N&#xd7;M pixels extending from a specified location within an input map to provide a block of N&#xd7;M pixels at an output port. A convolution engine reads blocks of pixels from the output port, combines blocks of pixels with a corresponding set of weights to provide a product, and subjects the product to an activation function to provide an output pixel value. The image cache comprises a plurality of interleaved memories capable of simultaneously providing the N&#xd7;M pixels at the output port in a single clock cycle. A controller provides a set of weights to the convolution engine before processing an input map, causes the convolution engine to scan across the input map by incrementing a specified location for successive blocks of pixels and generates an output map within the image cache by writing output pixel values to successive locations within the image cache.

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Brasov, Romania

Dragos Dinu