Single Object Classifier Hue Saturation Detection

The patent segments the detection process into two stages: first using a lightweight single object classifier to quickly identify potential regions, then applying more sophisticated analysis only to those specific regions. This segmentation allows the system to achieve high detection accuracy through the single object classifier while avoiding the computational burden of applying multi-object classifiers to entire images, thus resolving the contradiction between detection accuracy and computational complexity.

The patent applies partial action by using a simplified single object classifier for initial detection rather than the full multi-object classification process. This partial approach is sufficient for the initial screening phase, and only when needed does the system engage in more extensive analysis. This allows the system to achieve acceptable detection accuracy with significantly reduced computational complexity.

The patent makes the single object classifier universal by training it to detect multiple types of objects of interest (suspects, missing persons, stolen vehicles) using a unified detection framework. Rather than deploying separate specialized classifiers for each object type, the single classifier performs multiple functions through color-based detection and region analysis, thereby improving detection coverage while reducing device complexity.

The patent changes detection parameters (such as color thresholds, saturation levels, and region boundaries) to adapt the single object classifier for different object types and scenarios. By dynamically adjusting these parameters rather than using multiple fixed classifiers, the system achieves versatile detection coverage with a single adaptable classifier instance, reducing overall system complexity.