Real-Time Human Detection Using Relational Depth Similarity Features

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Space-Time Representation of People Based on 3D Skeletal Data: A Review (2016, methodology): “An alternate main methodology is starting with a ‘known’ prior, and fitting the silhouette or point cloud to this prior after the humans are localized [31], [82], [83].”
Human detection using depth information by Kinect (2011, methodology): “Ikemura et al. [6] proposed a window-based human detection method using relational depth similarity features based on depth information.”
People detection in RGB-D data (2011, methodology): “The contributions of this paper to the field of people detection are as follows: • We develop a robust dense depth person detection called Histogram of Oriented Depths (HOD) that takes inspiration from the method of Histogram of Oriented Gradients (HOG) and from the peculiar depth characteristics…”
Histogram of Oriented Normal Vectors for Object Recognition with a Depth Sensor (2012, methodology): “The Relational Depth Similarity Features (RDSF) [5] calculates features derived from a similarity of depth histograms that represents the relationship between two local regions using the Bhattacharyya distance [27].”
Spatio-temporal Depth Cuboid Similarity Feature for Activity Recognition Using Depth Camera (2013, background): “The self-similarity based feature has been proven to work well at object detection/retrieval and action detection on RGB data [20] and object recognition tasks on depth data [8, 9].”