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In many visual surveillance applications the task of person detection and localization can be solved easier by using thermal long-wave infrared (LWIR) cameras which are less affected by changing illumination or background texture than visual-optical cameras. Especially in outdoor scenes where usually only few hot spots appear in thermal infrared imagery, humans can be detected more reliably due to their prominent infrared signature. We propose a two-stage person recognition approach for LWIR images: (1) the application of Maximally Stable Extremal Regions (MSER) to detect hot spots instead of background subtraction or sliding window and (2) the verification of the detected hot spots using a Discrete Cosine Transform (DCT) based descriptor and a modified Random Naïve Bayes (RNB) classifier. The main contributions are the novel modified RNB classifier and the generality of our method. We achieve high detection rates for several different LWIR datasets with low resolution videos in real-time. While many papers in this topic are dealing with strong constraints such as considering only one dataset, assuming a stationary camera, or detecting only moving persons, we aim at avoiding such constraints to make our approach applicable with moving platforms such as Unmanned Ground Vehicles (UGV).

Wide Area Motion Imagery (WAMI) enables the surveillance of tens of square kilometers with one airborne sensor. Each image can contain thousands of moving objects. Applications such as driver behavior analysis or traffic monitoring require precise multiple object tracking that is dependent on initial detections. However, low object resolution, dense traffic, and imprecise image alignment lead to split, merged, and missing detections. No systematic evaluation of moving object detection exists so far although many approaches have been presented in the literature. This paper provides a detailed overview of existing methods for moving object detection in WAMI data. Also we propose a novel combination of short-term background subtraction and suppression of image alignment errors by pixel neighborhood consideration. In total, eleven methods are systematically evaluated using more than 160,000 ground truth detections of the WPAFB 2009 dataset. Best performance with respect to precision and recall is achieved by the proposed one

Automatic processing of videos coming from small UAVs offers high potential for advanced surveillance applications but is also very challenging. These challenges include camera motion, high object distance, varying object background, multiple objects near to each other, weak signalto-noise-ratio (SNR), or compression artifacts. In this paper, a video processing chain for detection, segmentation, and tracking of multiple moving objects is presented dealing with the mentioned challenges. The fundament is the detection of local image features, which are not stationary. By clustering these features and subsequent object segmentation, regions are generated representing object hypotheses. Multi-object tracking is introduced using a Kalman filter and considering the camera motion. Split or merged object regions are handled by fusion of the regions and the local features. Finally, a quantitative evaluation of object segmentation and tracking is provided.

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