Object Detection Sample Clauses

Object Detection. The first algorithmic step within the Image Analysis process was object detection. The object detection algorithms were developed incrementally over the course of the SUBITO project, with the performance of the algorithms tested at each iteration using representative data collected from the test site, and the results fed back in to the development process. Initially a background and static object detection algorithm was implemented, which detected static regions within the scene, i.e. baggage hypotheses, which were validated against various criteria to report potential abandoned baggage. The next phase improved the robustness of the baggage verification and developed a multi-view detection algorithm that would allow the detected object to be positioned in 3D space. The final phase implemented an enhanced baggage verification algorithm and the shift of the algorithm from a purely Central Processing Unit (CPU) based algorithm to an alternative Graphical Processing Unit (GPU) based algorithm which provided a considerable speed improvement. A number of component state-of-the-art algorithms were used in the object detection to provide robust object segmentation, specifically the detection of people and baggage within the scene. These were designed to provide robust background subtraction, static object detection and probable 3D location in space, utilising adaptive processes to automatically adapt to the current scene, and provide resilience against changes in the image, e.g. lighting levels. A fusion of detection from multiple views was also utilised to create a single world view of the likely location of detected objects. This multi-view approach reduced false alarms, [cf. Objective 2], and produced robust detections of individuals, Figure 2-6, and of baggage, Figure 2-7. The person and baggage detection algorithms developed during the project achieved the aims of providing robust localisation of moving objects alongside objects that have become stationary. Improvements to the state-of-the-art component algorithms, as demonstrated, have been implemented while being able to maintain a relatively fast rate (~6Hz) at which data could be output to the tracking module, [cf. Objective 3]. Further algorithmic improvements utilising Multi View-Infinite plane techniques have increased the speed of operation to between 15 – 30 fps.
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Related to Object Detection

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