In this paper, we study the estimation of motion direction prediction for fast motion and propose a threshold-based human target detection algorithm using motion vectors and other data as human target feature information. The motion vectors are partitioned into regions by normalization to form a motion vector field, which is then preprocessed, and then the human body target is detected through its motion vector region block-temporal correlation to detect the human body motion target. The experimental results show that the algorithm is effective in detecting human motion targets in videos with the camera relatively stationary. The algorithm predicts the human body position in the reference frame of the current frame in the video by forward mapping the motion vector of the current frame, then uses the motion vector direction angle histogram as a matching feature, and combines it with a region matching strategy to track the human body target in the predicted region, thus realizing the human body target tracking effect. The algorithm is experimentally proven to effectively track human motion targets in videos with relatively static backgrounds. To address the problem of sample diversity and lack of quantity in a multitarget tracking environment, a generative model based on the conditional variational self-encoder conditional generation of adversarial networks is proposed, and the performance of the generative model is verified using pedestrian reidentification and other datasets, and the experimental results show that the method can take advantage of the advantages of both models to improve the quality of the generated results.

中文翻译：

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一种基于运动方向预测的特定算法

在本文中，我们研究了快速运动的运动方向预测的估计，并提出了一种使用运动矢量和其他数据作为人类目标特征信息的基于阈值的人类目标检测算法。通过归一化将运动矢量划分为多个区域以形成运动矢量场，然后对该运动矢量场进行预处理，然后通过其运动矢量区域块-时间相关性检测人体目标以检测人体运动目标。实验结果表明，该算法可有效地在摄像机相对静止的情况下检测视频中的人体运动目标。该算法通过向前映射当前帧的运动矢量来预测视频中当前帧参考帧中的人体位置，然后将运动矢量方向角直方图作为匹配特征，并结合区域匹配策略在预测区域内跟踪人体目标，从而实现人体目标跟踪效果。实验证明该算法可有效跟踪背景相对静态的视频中的人体运动目标。针对多目标跟踪环境下样本多样性和数量不足的问题，提出了一种基于条件变分自编码器对抗网络条件生成的生成模型，并通过行人识别等方法验证了生成模型的性能。数据集和实验结果表明，该方法可以利用两个模型的优势来提高生成结果的质量。