Vehicle detection using multimodal imaging sensors from a moving platform

A modular vehicle detection system, using a two-stage hypothesis generation (HG) and hypothesis combination (HC) approach is presented. The HG stage consists of a set of simple algorithms which parse multi-modal data and provide a set of possible vehicle locations. These hypotheses are subsequently fused in a combination stage. This modular design allows the system to utilise additional modalities where available, and the combination of multiple information sources is shown to reduce false positive detections. The system uses Thales' high-resolution long wave infrared polarimeter and a four-band visible/near infrared multispectral system. Vehicle cues are taken from motion ow vectors, thermal intensity hot spots, and regions with a locally high degree of linear polarisation. Results using image sequences gathered from a moving vehicle are shown, and the performance of the system is assessed with Receiver Operator Characteristics.

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