Highest quality full cycle software development.
Expert in areas of Computer Vision, Multimedia, Messaging, Networking and others. Focused on embedded software development. Competent in building cross-platform solutions and distributed SW systems.
Offer standalone custom solutions as well as integration of existing products. Opened for outsourcing services.
Visit us at: http://www.rhondasoftware.com
This demo showcases real-time Human Pose Estimation, based on the Open Pose library, ported onto the camera platform, and designed by Rhonda’s Activity Recognition neural network for human behavior recognition. The two Deep Learning Neural Networks (DNN), along with the video pipeline, run on the Rhonda Software CV22 System on a Module (CV22 SoM).
Marketing researches are area where required to analyze a lot of data. E.g. we want to understand how many people are visiting a bank. In order to count this value, we need to count each man or woman which are entering to or exiting from the bank. For resolving this task there are a lot of approaches: e.g. use special gate with laser or mechanical counter. Though there are people counting tasks where such approaches cannot work or too unuseful. E.g. barrier cannot be used where people flow is very high, and laser counters have limitations as well.
Opposite the approaches above, we found papers where top-mounted camera is used for resolving the people counting task.
This object recognition algorithm is based on own pattern-matching algorithm. The algorithm is able to recognize pre-trained objects which are defined with special set of templates.
This is a demo video of Rhonda Audience Measurement system (MyAudience product, www.MyAudience.com).
Description
On this demo CV system tracks moving people using single PTZ (pan/tilt/zoom) camera (AXIS 214) and tries to positioning it to always keep first entered person in the camera sight. When PTZ stops in new position, the system filters out still objects from those that actually moving, assigns unique IDs (and color frames) to them and measures proximity of these objects to the original one using color-histogram-based algorithm. The object with highest proximity will be treated as a target. System will turn camera in the direction where targeting object moves, when it is approach to the border of camera sight (the red rectangle on the border indicates direction of next movement of PTZ cam).
Description
This video demo illustrates color-histogram-based object tracker in action. CV system tracks people as moving blobs (“clouds” of moving pixels) identifies them and distinct one from another in case of occlusions. When two (or more) blobs are intersected, system merges them in one combined object and marks it by IDs of all those source-objects that currently included in the combination. When one of objects separates from the combination CV system recognize which one is out and re-arrange ID appropriately. This approach works pretty well in case of characteristic histograms.