Approaching Object Detection Using Distance Sequence Comparison

The patent extracts only the essential elements needed for collision detection - specifically, distance measurements and object positions at different time instants - while discarding the complex recursive tracking machinery. By taking out only the critical distance comparison functionality and eliminating the need to maintain full object identities across frames, the system achieves collision detection with dramatically reduced computational requirements and energy consumption.

The patent segments the object tracking problem into independent distance measurement tasks for each detected object at each time instant, rather than maintaining continuous recursive tracking of object identities. This segmentation allows each frame's objects to be independently measured and compared, breaking the exponential computational complexity into manageable linear operations that can be executed with limited hardware resources.

The patent employs cheap, lightweight hardware components (such as standard cameras and basic processors) that would traditionally be insufficient for real-time collision detection. By using simplified algorithms that require minimal computational power, the system makes these low-cost, portable components effective for collision detection, enabling deployment in wearable devices, bicycles, and motorcycles without requiring high-performance hardware.

The patent changes the fundamental parameters of the detection approach by focusing solely on distance and position comparisons rather than full object recognition and tracking. This parameter change from complex image analysis to simple geometric measurements dramatically reduces the computational burden, allowing adequate response times to be achieved with minimal hardware complexity and enabling portability.