Pedestrian pre-collision pose is one of the key factors to determine the degree of pedestrian-vehicle injury in collision. Human pose estimation algorithm is an effective method to estimate pedestrian emergency pose from accident video.

To address this issue, some researchers [3,35,18]havetried toembed BCs into the ansatz.

Itcanbeshown (see [18] andthesupplementary material) thatKsEM-VROpt(n, )= KFIEMOpt(n, )= n2/3O( 1) and KsEM-VRCE (n, )= KFIEMCE (n, )= n+n2/3O( 1). SPIDER estimator employed, Proof Sketch.Whilewe

Supervised transfer learning has received considerable attention due to its potential to boost the predictive power of machine learning in scenarios where data are scarce.

The hope is that these predictions allow the algorithm to circumvent worst case lower bounds when the predictions are good, and approximately match them otherwise. The precise definitions and guarantees vary with different settings, but there have been significant successes in applying this framework for many different algorithmic problems, ranging from general online problems to classical graph algorithms (see Section 1.2 for a more detailed discussion of related work, and [35] for a survey). In all of these settings it turns out to be possible to define a "prediction" where the "quality" of the algorithm (competitive ratio, running time, etc.) depends the "error" of the prediction.

The Spiking Neural Network (SNN) is a biologically inspired neural network infrastructure that has recently garnered significant attention.