Activity/Action recognition aims to recognize the actions and goals of one or more agents from a series of observations on the agents' actions and the environmental conditions. Since the 1980s, this research field has captured the attention of several computer science communities due to its strength in providing personalized support for many different applications and its connection to many different fields of study such as medicine.
To understand activity recognition better, consider the following scenario. An elderly man wakes up at dawn in his small studio apartment, where he stays alone. He lights the stove to make a pot of tea, switches on the toaster oven, and takes some bread and jelly from the cupboard.
After taking his morning medication, a computer-generated voice gently reminds him to turn off the toaster. Later that day, his daughter accesses a secure website where she scans a check-list, which was created by a sensor network in her father's apartment. She finds that her father is eating normally, taking his medicine on schedule, and continuing to manage his daily life on his own. That information puts her mind at ease.
Many different applications have been studied by researchers in activity recognition; examples include assisting the sick and disabled. For example, Pollack et al. Show that by automatically monitoring human activities, home-based rehabilitation can be provided for people suffering from traumatic brain injuries. One can find applications ranging from security-related applications and logistics support to location-based services. Due to its many-faceted nature, different fields may refer to activity recognition as plan recognition, goal recognition, intent recognition, behavior recognition, location estimation and location-based services.
Applications:
-Monitoring Crowded Scenes:
Monitoring crowded urban environments is a vital goal for many of today's vision systems. Knowing the size
of crowds and tracking their motion has many applications. For example at traffic intersections, intelligent walk-signal systems could be designed based on the number of people waiting to cross. Also, the knowledge of the number of people walking through a crowded area, e.g., outside a school or outside the premises of a public event can be helpful in planning urban environments, general safety, and crowd control. We estimate accurately the counts of people in a scene without constraining ourselves to individuals. This includes dense groups of people moving together. We do this in real-time and place no constraints as far as camera placement or about the size of the groups as far as number of people. -Monitoring Bus Stops:
This clip shows some tracking results at a bus stop. The numbers in the boxes indicate the ID of the person being tracked.