Recent technical advances in Unmanned Aerial Vehicles (UAV) made a realm of applications possible. In this paper we focus on the application of following a walking pedestrian in real-time, using optimised pedestrian detection and object tracking. For this we use an on-board embedded system, offering an optimal ratio of computational power and weight. We extend the commonly used ground plane estimation technique, used to reduce the search space, based on the sensor data off the UAV. The integration of the ground plane constraint obtains a significant speed-up over the already optimised Aggregate Channel Feature (ACF) detector. To compensate for the frames without detections, we use a particle tracker based on color information. We successfully validated our system on a flying UAV.
Due to the wide applicability of pedestrian detection in surveillance and safety, this research topic has received much attention in computer vision literature. However, the focus of this research mainly lies in detecting and locating pedestrians individually as accurate as possible. In recent years, a number of datasets are captured using a forward looking camera from a car, which imposes the application of warning the driver when pedestrians are in front of the car. For such applications, it is not required to detect each pedestrian independently, but to generate an alarm when necessary. In this paper we explore techniques to boost the accuracy of recent channel-based algorithms in this application: algorithmic refinements as well as the inclusion of an LWIR image channel. We use the KAIST dataset which is constructed from image-pairs of both the visual and the LWIR spectrum, in day and night conditions. We study the influence of techniques that have shown success in literature.
In this paper we present a fast implementation of a robust object detector by using OpenCL. The use of fast object detection is of great use for a broad range of applications in multiple domains. OpenCL allows for scalability to more performant and different types of hardware, with minimal changes to the implementation. By using a GPU as execution device, we exploit the data parallelism opportunities of the algorithm. We also discuss the use of knowledge representation as a means to integrate expert knowledge into applications. This can be used both for faster processing by limiting the searching space, and for applications to work more autonomous by exploiting a higher level of intelligence.
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