Head detection and localization is a demanding task and a key element for many computer vision applications, like video surveillance, Human Computer Interaction and face analysis. The stunning amount of work done for detecting faces on RGB images, together with the availability of huge face datasets, allowed to setup very effective systems on that domain. However, due to illumination issues, infrared or depth cameras may be required in real applications. In this paper, we introduce a novel method for head detection on depth images that exploits the classification ability of deep learning approaches. In addition to reduce the dependency on the external illumination, depth images implicitly embed useful information to deal with the scale of the target objects. Two public datasets have been exploited: the first one, called Pandora, is used to train a deep binary classifier with face and non-face images. The second one, collected by Cornell University, is used to perform a cross-dataset test during daily activities in unconstrained environments. Experimental results show that the proposed method overcomes the performance of state-of-art methods working on depth images.
Very few works present approaches for head detection only based on depth images. The recent work of Chen @cite_17 presents a novel head descriptor to classify, through a (LDA) classifier, pixels as head or non-head. Depth values are exploited to eliminate false positives of head centers and to cluster pixels for final head detection. In the work of Nghiem @cite_22 , head detection is conducted on 3D data as first step for a fall detection system. This method detects only moving objects through background subtraction and all possible head positions are searched on contour segments. Then, modified HOG features @cite_20 are computed directly on depth data, to recognize people in the image. Finally, a SVM @cite_3 is exploited to create a head shoulder classifier. Even if the presence of other recent works that exploit CNNs with depth data @cite_10 @cite_25 @cite_5 @cite_27 , we believe that this paper proposes a novel approach for head detection on only depth maps.
@cite_22 @cite_3 @cite_27 @cite_5 @cite_10 @cite_25 @cite_20 @cite_17
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This article proposes a head detection algorithm for depth video provided by a Kinect camera and its application to fall detection. The proposed algorithm first detects possible head positions and then based on these positions, recognizes people by detecting the head and the shoulders. Searching for head positions is rapid because we only look for the head contour on the human outer contour. The human recognition is a modification of HOG (Histogram of Oriented Gradient) for the head and the shoulders. Compared with the original HOG, our algorithm is more robust to human articulation and back bending. The fall detection algorithm is based on the speed of the head and the body centroid and their distance to the ground. By using both the body centroid and the head, our algorithm is less affected by the centroid fluctuation. Besides, we also present a simple but effective method to verify the distance from the ground to the head and the centroid. A magnetization magnet (11) is brought into contact with or close to the outer circumferential surface of a tire (12) that has a steel belt in its outer circumferential portion, and is relatively moved along the circumferential direction of the tire (12) so that the magnetic field generated by the magnetization magnet (11) runs in the circumferential direction of the tire. (12), thereby magnetizing the tire (12). The entire perimeter of the outer circumferential portion of the tire (12) is continuously magnetized in one direction along the circumferential direction. After the polarities of the magnetization magnet (11) are inverted with respect to the circumferential direction of the tire (12), an angle range smaller than 360 DEG on the outer circumferential portion of the tire is continuously re-magnetized in a direction opposite to the one direction. In a magnetic field detection method, a magnetic field is differentially detected by two magnetic detection elements, which are set so that the magnetic field detection directions of the two magnetic detection elements are parallel to the side surface of the tire, and the two elements are juxtaposed in a direction perpendicular to the side surface of the tire. Recently, deep learning approaches have achieved promising results in various fields of computer vision. In this paper, we tackle the problem of head pose estimation through a Convolutional Neural Network (CNN). Differently from other proposals in the literature, the described system is able to work directly and based only on raw depth data. Moreover, the head pose estimation is solved as a regression problem and does not rely on visual facial features like facial landmarks. We tested our system on a well known public dataset, Biwi Kinect Head Pose, showing that our approach achieves state-of-art results and is able to meet real time performance requirements. An accurate and fast driver's head pose estimation is a rich source of information, in particular in the automotive context. Head pose is a key element for driver's behavior investigation, pose analysis, attention monitoring and also a useful component to improve the efficacy of Human-Car Interaction systems. In this paper, a Recurrent Neural Network is exploited to tackle the problem of driver head pose estimation, directly and only working on depth images to be more reliable in presence of varying or insufficient illumination. Experimental results, obtained from two public dataset, namely Biwi Kinect Head Pose and ICT-3DHP Database, prove the efficacy of the proposed method that overcomes state-of-art works. Besides, the entire system is implemented and tested on two embedded boards with real time performance. A correct and reliable localization of facial landmark enables several applications in many fields, ranging from Human Computer Interaction to video surveillance. For instance, it can provide a valuable input to monitor the driver physical state and attention level in automotive context. In this paper, we tackle the problem of facial landmark localization through a deep approach. The developed system runs in real time and, in particular, is more reliable than state-of-the-art competitors specially in presence of light changes and poor illumination, thanks to the use of depth images as input. We also collected and shared a new realistic dataset inside a car, called MotorMark, to train and test the system. In addition, we exploited the public Eurecom Kinect Face Dataset for the evaluation phase, achieving promising results both in terms of accuracy and computational speed. We study the question of feature sets for robust visual object recognition; adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection. We study the influence of each stage of the computation on performance, concluding that fine-scale gradients, fine orientation binning, relatively coarse spatial binning, and high-quality local contrast normalization in overlapping descriptor blocks are all important for good results. The new approach gives near-perfect separation on the original MIT pedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds. Head detection may be more demanding than face recognition and pedestrian detection in the scenarios where a face turns away or body parts are occluded in the view of a sensor, but locating people is needed. In this paper, we introduce an efficient head detection approach for single depth images at low computational expense. First, a novel head descriptor is developed and used to classify pixels as head or non-head. We use depth values to guide each window size, to eliminate false positives of head centers, and to cluster head pixels, which significantly reduce the computation costs of searching for appropriate parameters. High head detection performance was achieved in experiments − 90 accuracy for our dataset containing heads with different body postures, head poses, and distances to a Kinect2 sensor, and above 70 precision on a public dataset composed of a few daily activities, which is higher than using a head-shoulder detector with HOG feature for depth images.