Object tracking has been widely used in various intelligent systems, such as pedestrian tracking, autonomous vehicles. To solve the problem that appearance changes and occlusion may lead to poor tracking performance, we propose a multiple instance learning (MIL) based method for object tracking. To achieve this task, we first manually label the first several frames of video stream in image level, which can indicate that whether a target object in the video stream. Then, we leverage a pre-trained convolutional neural network that has rich prior information to extract deep representation of target object. Since the location of the same object in adjacent frames is similar, we introduce a particle filter to predict the location of target object within a specific region. Comprehensive experiments have shown the effectiveness of our proposed method.

对象跟踪已广泛用于各种智能系统，例如行人跟踪，自动驾驶汽车。为了解决外观变化和遮挡可能导致跟踪性能差的问题，我们提出了一种基于多实例学习（MIL）的对象跟踪方法。为了实现此任务，我们首先手动在图像级别标记视频流的前几帧，这可以指示视频流中是否有目标对象。然后，我们利用具有丰富先验信息的预训练卷积神经网络来提取目标对象的深层表示。由于同一对象在相邻帧中的位置相似，因此我们引入了粒子过滤器来预测目标对象在特定区域内的位置。综合实验证明了我们提出的方法的有效性。