Tech companies are shifting more towards machine learning or AI-first strategy. What does that mean to them? What does that mean to us? Growing number of aging population and shrinking funding are putting enormous pressure to the overall healthcare system for keeping up with the desired quality of care with limited resources. Thus, there is an increasing focus on machine learning capabilities for just-in-time alerts to predict various future events so that undesirable incidents can be reduced by giving attention to the right people at the right time. This talk explores the underlying framework behind such capabilities, various strategies for a resilient system, and the role of a machine learning platform. Continue reading

The goal of a video analytics system or application is to generate insights from an observed video stream. In a video analytics system, a continuous stream of video frames is the input which might be originated from various sources, e.g., web cam, mobile camera, Kinect sensor, security camera, video file etc. The output could be one or more video frames per input frame or some data representing insights or perception of the observed scene. Depending on the application, the output video frames might be annotated. For instance, in a face detection application (typically available in cameras), detected faces are highlighted with rectangle; in an object detection system (surveillance applications), moving objects are highlighted and might be annotated with object class such as person, group, car etc.  Continue reading

Ground truth images are essential for the evaluation of object detection or background subtraction techniques. Now what is a ground truth image? Let’s consider a video frame captured using a fixed camera where some objects are static while some are moving. A ground truth image corresponding to this image labels the static and moving parts with two different labels. Usually, white pixels are used for moving objects and black pixels for static pixels. The goal of an object detection technique is to produce detection image close to the ground truth image. To compare the performance of two or more object detection techniques, detection images are compared with the corresponding ground truth images. Most standard datasets come with ground truth images for the test sequences. However, the availability of ground truth images is not uniform across all datasets. For example, some datasets provide ground truth image for just one test frame only while some other datasets provide for all video frames. On the other hand, if you want to want to evaluate performance on your own datasets, there would be no ground truth images at all. A technique to manually generate the ground truth image for a video frame is explained below. Continue reading