智能技术学报

文章详情

稿件标题: Scene-adaptive hierarchical data association and depth-invariant part-based appearance model for indoor multiple objects tracking
稿件作者: Hong Liu, Can Wang, Yuan Gao
关键字词: Multiple objects tracking; Scene-adaptive; Data association; Appearance model; RGB-D data
文章摘要: Indoor multi-tracking is more challenging compared with outdoor tasks due to frequent occlusion, view-truncation, severe scale change and pose variation, which may bring considerable unreliability and ambiguity to target representation and data association. So discriminative and reliable target representation is vital for accurate data association in multi-tracking. Pervious works always combine bunch of features to increase the discriminative power, but this is prone to error accumulation and unnecessary computational cost, which may increase ambiguity on the contrary. Moreover, reliability of a same feature in different scenes may vary a lot, especially for currently widespread network cameras, which are settled in various and complex indoor scenes, previous fixed feature selection schemes cannot meet general requirements. To properly handle these problems, first, we propose a scene-adaptive hierarchical data association scheme, which adaptively selects features with higher reliabili
收录刊物: 2016年1卷3期
稿件基金:
浏览次数: 275
下载次数: 63
点击下载