Automatic Video Surveillance System (AVSS) has become important to computer vision researchers as crime in public places has increased in the twenty first century. As a new branch of AVSS, baggage detection and classification has a broad area of security applications. Some of them are, detecting carriage of illegal materials into baggage, detecting unclaimed baggage in public space that can be placed by terrorists for violence, detecting baggage in baggage restricted super shop etc. However, in this paper, a detection & classification framework of baggage is proposed using dynamic human body parameter with boosting strategy. Initially, background subtraction is performed instead of sliding window approach to speed up the system and HSI model is used to deal with uneven illumination condition. Then, to overcome the shadow effect a model is introduced. Extraction of rotational signal descriptor (RSD_HOG) from Region of Interest (ROI) added efficiency in HOG. Finally, dynamic approach in human body parameter setting enabled the system to detect & classify single or multiple carried baggages although some portions of human are absent. In baggage detection, boosting of similarity measure based cascade multilayer SVMs into HOG based SVM generated a strong classifier. This scheme has used to deal with various texture patterns of baggages. Experimental results discovered the system satisfactorily accurate and faster comparative to other alternatives.

Patient data collection and emergency health service is the primary challenge in developing countries. Risk assessment of pregnant mother and healthcare based on priority is almost impossible in present health service of Bangladesh. A pilot study was done in three urban slums of Dhaka where BRAC health workers were provided with mobiles. A smart algorithm was incorporated in the mobiles. The mobile solution came up with useful findings. The health workers now could send data directly to central MIS system which reduced previous time lag. A secure web page contained all the patient data which was accessible by BRAC Personnel from anywhere any time. An automated risk assessing decision tree categorized the patients depending on their risk levels for timely treatment. The mobile solution proposed a pro-active, cost-effective platform for rapid health service for pregnant mothers and neonates with reduced manpower