TY - Jour Au - An，Feng-ping Py - 2019 DA - 2019/10/14 TI - 步行重新识别算法基于优化深度学习序列记忆型号SP - 5069026 VL - 2019年 - 行人重新识别是一个重要研究是因为它影响智能监控，基于内容的视频检索和人机交互等应用。它可以帮助大规模视频监控系统中的继电跟踪和犯罪嫌疑人检测。虽然现有的传统行人重新识别方法已被广泛应用于解决实际问题，但它们具有低识别准确性，低效计算等缺陷，且难以适应特定应用。近年来，由于其强大的自适应能力和高识别准确性，人们基于深度学习的行人重新识别算法已广泛应用于行人重新识别领域。深度学习模式为行人重新认可任务提供了具有强大的学习能力的技术方法。然而，基于深度学习的行人重新识别方法也存在以下问题：首先，现有的深度学习行人重新识别方法缺乏记忆和预测机制，深入学习方法只提供有限的人行力重新识别准确性的改进。其次，他们表现出过度装备的问题。最后，初始化现有的LSTM参数是有问题的。鉴于此，本文介绍了进入行人重新识别探测器的可恢复连接，使其通过将单个图像转换为图像序列来更类似于人类认知过程; then, the memory image sequence pattern reidentifies the pedestrian image. This approach endows deep learning-based pedestrian re-recognition algorithms with the ability to memorize image sequence patterns and allows them to reidentify pedestrians in images. At the same time, this paper proposes a selective dropout method for shallow learning. Selective dropout uses the classifier obtained through shallow learning to modify the probability that a node weight in the hidden layer is set to 0, thereby eliminating the overfitting phenomenon of the deep learning model. Therefore, this paper also proposes a greedy layer-by-layer pretraining algorithm for initializing LSTM and obtains better generalization performance. Based on the above explanation, this paper proposes a pedestrian re-recognition algorithm based on an optimized LSTM deep learning-sequence memory learning model. Experiments show that the pedestrian re-recognition method proposed in this paper not only has strong self-adaptive ability but also identifies the average accuracy. The proposed method also demonstrates a significant improvement compared with other mainstream methods because it can better memorize and learn the continuous motion of pedestrians and effectively avoid overfitting and parameter initialization in the deep learning model. This proposal provides a technical method and approach for adaptive pedestrian re-recognition algorithms. SN - 1076-2787 UR - https://doi.org/10.1155/2019/5069026 DO - 10.1155/2019/5069026 JF - Complexity PB - Hindawi KW - ER -