Automated Multi-Frame Object Tracking with Dynamic Search Range

Moving object tracking across multiple frames is automated provided that inter-frame displacements remain within ±(10 + "X Range") pixels in the X-direction and ±(10 + "Y Range") pixels in the Y-direction. This automation is achieved through a combination of a relatively large search region for locating optimal new positions of specific control points, coupled with strong forces imposed by high image gradient values. The implementation typically employs a search algorithm that progressively refines control point positions using gradient magnitude and direction information.

During the tracking process, continuous monitoring of the moving object is maintained with real-time adjustments based on per-frame variations. This adaptive approach ensures precise capture of the object's movement trajectory and accurate determination of its final position. Algorithmically, this may involve implementing a Kalman filter or optical flow-based predictor to anticipate position changes between frames.

Furthermore, the system utilizes image gradient analysis to determine object velocity and direction. Higher gradient magnitudes correspond to faster movement, while gradient direction vectors indicate motion orientation. These metrics are computationally derived through convolution operations (e.g., using Sobel or Scharr operators) and integrated into the tracking logic to enhance motion path interpretation. The gradient data can be processed through directional histogram analysis or vector field calculations to refine trajectory predictions.