Human detection has received great attention during the past few decades, which is yet still a challenging problem. In this paper, we focus on the problem of 3-D human detection, i.e., finding the human bodies and determining their 3-D coordinates in complex 3-D space using depth data only. Since the traditional sliding-window-based approaches for target localization are time-consuming and the recent deep-learning-based object detectors generate too many region proposals, we propose to utilize the candidate head-top locating stage to efficiently and quickly find the plausible head-top locations. In the second stage, we propose a Depth map, Multiorder depth template, and Height difference map representation encoding three channels of information for each candidate region to utilize the neural network pretrained on large-scale well-annotated datasets to classify the candidate regions. We evaluate our method on four publicly available challenging datasets. Extensive experimental results demonstrate that the proposed method is superior to the state-of-the-art methods while achieving real-time performance.

Efficient and robust detection of humans has received great attention during the past few decades. This paper presents a two-staged approach for human detection in RGB-D images. As the traditional sliding window-based methods for target localization are often time-consuming, we propose to use the super-pixel method in depth data to efficiently locate the plausible head-top locations in the first stage. In the second stage, we propose to use Random Ferns to seek the features by combining information from different image spaces, which can select the most discriminative features and compute simple and fast Local Binary Features (LBFs) allowing for real-time applications. We evaluate our method on three publicly available challenging datasets taken by a Kinect camera. Experimental results demonstrate that the proposed approach can robustly detect humans in complicated environments.

Real-time human detection is important for a wide range of applications. In this paper, a two-staged method has been developed for real-time human detection in cluttered and dynamic environments with depth data. We start with generating a set of possible human head-tops to ensure all human locations are included. To this end, a novel physical radius-depth (PRD) detector is proposed to quickly detect human candidates. The second stage applies a convolutional neural network (CNN), aiming at extracting feature of human upper body automatically instead of hand-crafting, and then on the basis of CNN feature genuine human candidates are preserved while false ones are filtered out. Experiment results on four publicly available datasets, including a dataset under weak illumination or even total darkness, show that the proposed method can reliably detect human in RGB-D videos in real time without GPU acceleration, and yields higher accuracy than the compared state-of-the-art approaches.

Perception of the presence and position of human is crucial for many kinds of Artificial Intelligence (AI) applications. In this paper, we have developed a novel two-staged method for realtime human detection in depth image. The first stage is to quickly scan through the image to detect possible head-top locations in order to ensure all the candidate locations are included. The second stage is to use a novel head-shoulder descriptor (HSD) which jointly encodes the One-hot Depth Difference information and local geometric characteristics of human upper body to filter the detections so as to keep the genuine human locations and discard false positives. The results show that our approach using only depth data is superior to other methods using color and depth images on four datasets. In addition, our method performs well under weak illumination conditions or even total darkness. Moreover, our system is also able to run in real-time on conventional PC without GPU acceleration.

This paper proposes a two-staged approach to real-time human detection in cluttered environments using RGB-D camera. The first stage is a novel physical blob (P-Blob) detection that can quickly find plausible human heads. The second stage uses a combination of human upper-body features to filter out false positives. Experiment results on three publicly available datasets show that the proposed method can reliably detect people in RGB-D video in real time.

Real-time human detection in crowded and dynamic environments poses a significant challenge, due to complex background, occlusion and different human poses. In this paper, we propose a two-staged approach using color and depth data taken by an RGB-D camera. The first stage is to find plausible head-top locations quickly in depth image. The second stage is to extract effective discrimination features from RGB-D data to discard the false positives with support vector machine. The experiments on the publicly available office dataset, mobile platform dataset and clothing store dataset show that the system achieves accurate and real-time detection results on a single CPU core and its performance is significantly superior to state-of-the-art methods.