基于视频的装甲车和飞机检测跟踪及轨迹预测算法

针对当前视频目标跟踪算法跟踪多目标容易跟丢的问题，以视频中的装甲车、飞机为研究对象，研究一种改进跟踪学习检测（TLD）的视频多目标检测跟踪算法。对于跟丢的目标，利用Kalman滤波算法的预测功能跟踪视频中典型目标的轨迹，并采用Kalman滤波算法跟踪的轨迹来弥补TLD算法丢失的部分，从而获得视频中典型目标的完整轨迹，以提高视频多目标跟踪的准确率。由于现有轨迹预测算法存在准确性较差的局限性，提出一种基于社交长短时记忆（Social-LSTM）网络的视频典型目标轨迹预测算法，将上下文环境信息和多个目标轨迹之间的相互影响关系融入Social-LSTM网络，预测待检测典型目标的轨迹序列。仿真实验结果表明，所提轨迹预测算法优于传统的LSTM算法、隐马尔可夫模型算法以及混合高斯模型算法，有利于提高视频典型目标轨迹预测的准确率。

A Video-based Prediction Algorithm for Armored Vehicle and Aircraft Detection/tracking and Trajectory

To solve the problem of easily losing the targets when tracking multiple targets by the current video target tracking algorithms，an improved tracking-learning-detection (TLD) algorithm is presented for multi-target detection and tracking by taking armored vehicles and aircrafts in videos as the research objects.For the lost targets，the trajectories of typical targets in video are tracked by using the prediction function of Kalman filtering algorithm，and the tracked trajectories are used to compensate for the lost parts of TLD algorithm so as to obtain the complete trajectories of typical targets in videos，which is beneficial to improve the accuracy of video multi-target tracking.For the poorer accuracy of the existing trajectory prediction methods，a video target trajectory prediction algorithm based on social long short term memory (Social-LSTM) network is proposed. The algorithm integrates the contextual environment information and the interaction relationship among multiple target trajectories into Social-LSTM network and predicts the trajectories of the typical targets to be detected. Simulation experimental results show the trajectory prediction algorithm is superior to the traditional LSTM algorithm，hidden Markov model (HMM) algorithm，and Gaussian mixture model (GMM) algorithm，which is helpful to improve the accuracy of trajectory prediction for typical video targets.