Abstract
This paper proposes the Parallel WiSARD Object Tracker (PWOT), a new object tracker based on the WiSARD weightless neural network that is robust against quantization errors. Object tracking in video is an important and challenging task in many applications. Difficulties can arise due to weather conditions, target trajectory and appearance, occlusions, lighting conditions and noise. Tracking is a high-level application and requires the object location frame by frame in real time. This paper proposes a fast hybrid image segmentation (threshold and edge detection) in YcbCr color model and a parallel RAM based discriminator that improves efficiency when quantization errors occur. The original WiSARD training algorithm was changed to allow the tracking.
Highlights
Surface targets tracking is a task of great importance for warships
This paper presents an efficient video target tracker based on the WiSARD weightless neural network, which is able to work in real time and can compensate quantization errors generated by the image segmentation
The proposed Parallel WiSARD Object Tracker (PWOT) [7] has tree components: an object detector ObjDet based on the WiSARD weightless neural networks (WNN), a second RAM memory (RAM2) and a position predictor
Summary
Surface targets (ships or enemy vessels) tracking is a task of great importance for warships. This paper presents an efficient video target tracker based on the WiSARD weightless neural network, which is able to work in real time and can compensate quantization errors generated by the image segmentation. The proposed Parallel WiSARD Object Tracker (PWOT) [7] (figure 1) has tree components: an object detector ObjDet based on the WiSARD WNN (first RAM memory), a second RAM memory (RAM2) and a position predictor. All discriminators are trained with the quantized pixels (target model) inside a frame region defined manually by the operator, the selection window (SLW). The ObjDet receives as input the quantization result of all pixels inside the ROI. Each discriminator tries to recognize the target in a different region inside ROI. We used an outdated computer to test the effectiveness of this approach on a lagged device
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