Detecting carried objects is one of the requirements for developing systems to reason about activities involving people and objects. We present an approach to detect carried objects from a single video frame with a novel method that incorporates features from multiple scales. Initially, a foreground mask in a video frame is segmented into multi-scale superpixels. Then the human-like regions in the segmented area are identified by matching a set of extracted features from superpixels against learned features in a codebook. A carried object probability map is generated using the complement of the matching probabilities of superpixels to human-like regions and background information. A group of superpixels with high carried object probability and strong edge support is then merged to obtain the shape of the carried object. We applied our method to two challenging datasets, and results show that our method is competitive with or better than the state-of-the-art.

翻译：检测携带的物体是开发系统以了解涉及人和物体的活动的要求之一。 我们展示了一种方法,从单一的视频框中探测携带的物体,采用一种包含多个比例特征的新颖方法。 最初, 视频框中的表面面罩被分割成多尺度的超级像素。 然后, 通过将从超级像素中提取的一套特征与在代码簿中学习到的特征相匹配, 来识别分区区域内的类似人类的区域。 一个携带的物体概率图是利用超像素与类似人类的区域和背景信息相匹配的概率来生成的。 随后, 将一组携带对象概率高且边缘支持强的超像素组合在一起, 以获得被移动对象的形状。 我们用我们的方法对两个具有挑战性的数据集进行了区分, 结果显示我们的方法与最先进的数据集具有竞争力或更好竞争力。