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This paper presents a video analytics algorithm for detecting event of objects crossing predetermined line-of-interest in the scene in specific direction. A fast blob-based analysis is formulated to detect the event, combined with the object detection and tracking to detect and tracked the object as motion blobs. Proposed algorithm is tested in real outdoor surveillance environment for 24 hours in 3 days to evaluate the detection accuracies in different scenarios. For comparison, the testing is done against a commercial surveillance system. The results show that the proposed algorithm provides better accuracy in all scenarios, while maintaining real-time processing capacity.

Aerial mapping is attracting more attention due to the development in unmanned aerial vehicles (UAVs) and their availability and also vast applications that require a wide aerial photograph of a region in a specific time. The cross-modality as well as translation, rotation, scale change and illumination are the main challenges in aerial image registration. This paper concentrates on an algorithm for aerial image registration to overcome the aforementioned issues. The proposed method is able to sample automatically and align the sensed images to form the final map. The results are compared with satellite images that shows a reasonable performance with geometrically correct registration.

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