This paper investigates the impact of pixel-level fusion of videos from visible (VIZ) and infrared (IR) surveillance cameras on object tracking performance, as compared to tracking in single modality videos. Tracking has been accomplished by means of a particle filter which fuses a colour cue and the structural similarity measure (SSIM). The highest tracking accuracy has been obtained in IR sequences, whereas the VIZ video showed the worst tracking performance due to higher levels of clutter. However, metrics for fusion assessment clearly point towards the supremacy of the multiresolutional methods, especially Dual Tree-Complex Wavelet Transform method. Thus, a new, tracking-oriented metric is needed that is able to accurately assess how fusion affects the performance of the tracker.

The widespread adoption of digital video techniques has generated a requirement for authenticity verification in applications such as criminal evidence, insurance claims and commercial databases. This work addresses problems that arise from a spatial digital watermarking technique developed to detect frame reordering and dropping scenarios. It discusses the differences between mutual frame types at different bit-rates. Many papers consider detection after MPEG-2 decoding as a naïve approach. However, this approach does offer significant advantages for a slight increase of computation load. This paper also establishes a link between the detector performance and the sequence content. The uniqueness of this work is the comparison of the test results using 18 different standard MPEG test-sequences. The functionality of the algorithm is demonstrated with a simulated attack.