This study sought to validate the Short Grit Scale (Grit-S), an instrument that measures perseverance and passion for long-term goals, among Chinese high school students. Confirmatory factor analyses revealed that the scale retains the two-factor structure of the original scale. The scale demonstrated satisfactory internal consistency and test–retest reliability. Evidence for construct validity was found in relation to the Big Five personality traits, self-control, and IQ. Evidence for criterion validity was found via the observation that grit explained unique variance in academic performance. Together, the Grit-S is a sound measure of grit among Chinese adolescents.

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Most multi-object tracking methods have achieved good results in tracking multiple pedestrians with Kalman filter, but their tracking performance in crowded scenes is still poor due to pedestrian avoidance and frequent occlusion. In crowded scenes, the pedestrian trajectory prediction with Kalman filter alone is unreliable. In this paper, a two-dimensional field-of-view avoidance force model (AFM) is proposed to assist the Kalman filter prediction by sensing the avoidance force and then complete pedestrian tracking. In the model, each pedestrian has a two-dimensional field of view to perceive the avoidance force, which determines the next predicted trajectory. In real scenes, pedestrians tend to be more concerned about the surrounding area, so different areas are set to simulate the attention mechanisms of pedestrians in real scenes. In the FairMOT model, AFM is used to optimize the pedestrian state values of Kalman filter prediction and the optimized model is trained on the MOT16 public dataset. The experimental results on the MOT20 benchmark dataset show that compared with the mainstream tracking model FairMOT, our method respectively improves MOTA by 2.7% and IDF1 by 2.2%. Our method also achieves the good performance on MOT15, MOT16, and MOT17 tracking benchmarks.