Placemeter Inc.

Canon Kabushiki Kaisha

Tuan Hue Thi

Niklaus Haldimann

Alexandre Winter

Ugo Jardonnet

Anthony Knittel

Embodiments of a method and system described herein enable capture of video data streams from multiple, different video data source devices and the processing of the video data streams. The video data streams are merged such that various data protocols can all be processed with the same worker processors on different types of operating systems, which are typically distributed. In an embodiment the multiple video data sources comprises at least one mobile device executing a video sensing application that produces a video data stream for processing by video analysis worker processes. The processes include automatically detecting features in an urban scene comprising sidewalks, roads, bike routes and road crossings.

Systems and methods for processing video data for activity monitoring

Embodiments of a method and system described herein enable capture of video data streams from multiple, different video data source devices and the processing of the video data streams. The video data streams are merged such that various data protocols can all be processed with the same worker processors on different types of operating systems, which are typically distributed. In an embodiment the multiple video data sources comprises at least one mobile device executing a video sensing application that produces a video data stream for processing by video analysis worker processes. The processes include automatically detecting moving objects in a video data stream, and further tracking and analyzing the moving objects.

Robust, adaptive and efficient object detection, classification and tracking

Embodiments of a method and system described herein enable capture of video data streams from multiple, different video data source devices and the processing of the video data streams. The video data streams are merged such that various data protocols can all be processed with the same worker processors on different types of operating systems, which are typically distributed. In an embodiment the multiple video data sources comprises at least one mobile device executing a video sensing application that produces a video data stream for processing by video analysis worker processes. The processes include estimating a queue wait time.

Method for queue time estimation

Embodiments of a method and system described herein enable capture of video data streams from multiple, different video data source devices and the processing of the video data streams. The video data streams are merged such that various data protocols can all be processed with the same worker processors on different types of operating systems, which are typically distributed. In an embodiment the multiple video data sources comprises at least one mobile device executing a video sensing application that produces a video data stream for processing by video analysis worker processes. The processes include automatically detecting features in an urban scene comprising building entrances.

Automated detection of building entrances

Embodiments of a method and system described herein enable capture of video data streams from multiple, different devices and the processing of the video data streams. The video data streams are merged such that various data protocols can all be processed with the same worker processors on different types of operating systems, which are typically distributed. An embodiment uses a mobile device (such as a mobile phone) as a device and deploys a video sensor application on the mobile device for encoding consecutive video files, time stamping the consecutive video files, and pushing the consecutive video files to a file server to produce a stable stream of video data. Thus avoiding the inefficiencies associated with having video processing in the data flow loop.

System and method for activity monitoring using video data

A method of training an artificial neural network The artificial neural network comprising a plurality of connections connecting nodes arranged in at least an initial and a subsequent layer. The method comprises receiving a training example, the training example having input values, a target, and an associated training salience value indicating an importance of the input values to determining the target. The method further comprises determining salience values for nodes of the initial layer from the training salience value; determining an activation value for at least one node of the subsequent layer by propagating the input values using a subset of the connections selected based on the determined salience values for the nodes of the initial layer; and training the artificial neural network using the activation value, the trained artificial neural network configured to determine a relationship between the input data values and the target.

Method for training an artificial neural network

Embodiments include a system and method for activity monitoring using video data from multiple dissimilar sources. The video data is processed to remove any dependency of the system on types of video input data. The video data is processed to yield useful human readable information regarding events in real time, such as how many people move through a line in a period of time.

Virtual turnstile system and method

Embodiments of a method and system described herein enable capture of video data streams from multiple, different video data source devices and the processing of the video data streams. The video data streams are merged such that various data protocols can all be processed with the same worker processors on different types of operating systems, which are typically distributed.

Growing community of inventors

New York, NY, United States of America

Tuan Hue Thi