论文标题
使用外观模型和视觉对象跟踪在高速公路中的多车跟踪
Multiple-Vehicle Tracking in the Highway Using Appearance Model and Visual Object Tracking
论文作者
论文摘要
近几十年来，由于机器视觉的开创性改进，计算机执行了许多日常任务。这些任务之一是多车跟踪，该跟踪广泛用于视频监视和交通监控等不同区域。本文着重于以可接受的精度引入有效的新颖方法。这是通过从每个对象提取的功能的有效外观和运动模型来实现的。为此，已经使用了两种不同的方法来提取功能，即从深度神经网络中提取的功能以及传统功能。然后将这两种方法的结果与最先进的跟踪器进行了比较。通过在UA-Detrack基准测试上执行方法来获得结果。第一种方法导致58.9％的准确性，而第二种方法则导致高达15.9％。提取更明显的特征仍然可以改善所提出的方法。
In recent decades, due to the groundbreaking improvements in machine vision, many daily tasks are performed by computers. One of these tasks is multiple-vehicle tracking, which is widely used in different areas such as video surveillance and traffic monitoring. This paper focuses on introducing an efficient novel approach with acceptable accuracy. This is achieved through an efficient appearance and motion model based on the features extracted from each object. For this purpose, two different approaches have been used to extract features, i.e. features extracted from a deep neural network, and traditional features. Then the results from these two approaches are compared with state-of-the-art trackers. The results are obtained by executing the methods on the UA-DETRACK benchmark. The first method led to 58.9% accuracy while the second method caused up to 15.9%. The proposed methods can still be improved by extracting more distinguishable features.
