Traffic Sign Recognition System

The traffic sign recognition system, an intelligent vision system for recognizing the traffic signs, becomes important in an autonomous vehicle. It will provide information of the road signs on the way and guide the vehicle when running in the street. In driver assistance systems, it helps the driver to recognize the road signs early and accurately. A false recognition of the traffic signs caused by the human factors could be avoided. Thus, the traffic sign recognition system makes the driving safer and easier.

In the outdoor environment, due to the illumination changes, rotation, shadows, and partial occlusion of the images, the traffic sign recognition becomes a challenging task. Furthermore, a fast algorithm is needed to operate in the real time environment. Generally, the traffic sign recognition system is divided into two stages: the detection stage, which finds the region of interest containing the traffic signs from an image, and the classification stage where the detected signs are classified into one of the road signs.

Source : A. Soetedjo, K. Yamada"Fast and Robust Traffic Sign Detection",2005 IEEE International Conference on Systems, Man and Cybernetics, Hawaii, October 2005

Human Activity Recognition

   

Activity recognition is an important technology in pervasive computing because it can be applied to many real-life, human-centric problems such as elder care and health care. Successful research has so far focused on recognizing simple human activities. Recognizing complex activities remains a challenging and active area of research. Specifically, the nature of human activities poses the following challenges:

• Recognizing concurrent activities: People can do several activities at the same time. For example, people can watch television while talking to their friends. These behaviors should be recognized using a different approach from that for sequential activity.
• Recognizing interleaved activities: Certain real life activities may be interleaved. For instance, while cooking, if there is a call from a friend, people pause cooking for a while and after talking to their friend, they come back to the kitchen and continue to cook.
• Ambiguity of interpretation:Depending on the situation, the interpretation of similar activities may be different. For example, an activity “open refrigerator” can belong to several activities, such as “cooking” or “cleaning”.
• Multiple residents: In many environments more than one resident is present. The activities that are being performed by the residents in parallel need to be recognized, even if the activity is performed together by the residents in a group.

Once activities are discovered, they can provide the basis for a model to recognize the activity, track its occurrence, and even use the information to assess an individual's wellbeing or provide activity-aware services. These activity discovery and recognition technologies are thus valuable for providing pervasive assistance in an individual's everyday environments.

 Source: E. Kim, S. Helal and D. Cook:" Human Activity Recognition and Pattern Discovery"