The Alpha-Beta Family of Filters to Solve the Threshold Problem: A Comparison

Abstract

Typically, devices work to improve life quality, measure parameters, and make decisions. They also signalize statuses, and take actions accordingly. When working, they measure different values. These are to be compared against thresholds. Some time ago, vision systems came into play. They use camera(s) to deliver(s) images to a processor module. The received images are processed to perform detections (typically, they focus to detect objects, pedestrians, mopeds, cyclists, etc.). Images are analyzed and thresholds are used to compare the computed values. The important thing is that images are affected by noise. Therefore, the vision system performance can be affected by weather in some applications (for example, in automotive). An interesting case in this domain is when the measured/computed values show small variations near the threshold (not exceeding) but very close to it. The system is not able to signalize/declare a state in this case. It is also important to mention that changing the threshold does not guarantee solving the problem in any future case, since this may happen again. This paper proposes the Alpha-Beta family of filters as a solution to this problem. The members can track a signal based on measured values. This reveals errors when the tracked-signal’s first derivative changes sign. These errors are used in this paper to bypass the threshold problem. Since these errors appear in both situations (when the first derivative decreases from positive to negative and increases from negative to positive), the proposed method works when the observed data are in the vicinity of the threshold but above it.

D-STGCN: Dynamic Pedestrian Trajectory Prediction Using Spatio-Temporal Graph Convolutional Networks

“…In future work, previous [48] and current results will be considered for future developments aimed at addressing the more complicated real-world situations commonly encountered in autonomous driving. Furthermore, it is interesting to determine the performance obtained using a larger dataset such as the nuScenes dataset [49].…”