Track Before Detect of Low SNR Objects in a Sequence of Image Frames Using Particle Filter
This work addresses detection and tracking challenges in noisy environments, but it appears incremental as it builds on existing particle filter methods without claiming major breakthroughs.
The paper tackles the problem of detecting and tracking low signal-to-noise ratio objects in image sequences with noise and clutter, using a multiple model track-before-detect particle filter approach, and evaluates its performance in various scenarios.
A multiple model track-before-detect (TBD) particle filter-based approach for detection and tracking of low signal to noise ratio (SNR) objects based on a sequence of image frames in the presence of noise and clutter is briefly studied in this letter. At each time instance after receiving a frame of image, first, some preprocessing approaches are applied to the image. Then, it is sent to the multiple model TBD particle filter for detection and tracking of an object. Performance of the approach is evaluated for detection and tracking of an object in different scenarios including noise and clutter.