Parallel WiSARD object tracker: a ram-based tracking system
This work addresses object tracking challenges such as weather conditions and occlusions for real-time video applications, but it appears incremental as it modifies an existing WiSARD method for tracking.
The paper tackles object tracking in video by proposing the Parallel WiSARD Object Tracker (PWOT), a system based on a weightless neural network that is robust against quantization errors, achieving improved efficiency through a fast hybrid image segmentation and parallel RAM-based discriminator.
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.