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Lying-pose human detection is an active research field of computer vision in recent years. It has a good theoretical significance and furthermore many applications, such as victim detection or home service robot. But the study on lying-pose human detection in low-altitude overlooking images have many unsolved problems owing to multiple poses, arbitrary orientation, in-plane rotation, perspective distortion, and time-consuming. In this paper, the proposed framework of human lying-pose detection is optimization and machine learning algorithms inspired by processes of neurobiology suggest and human vision system to select possible object locations. First, the proposed model effectively utilizes binarized normed gradient features to obtain the objectness rapidly based on the vision saliency. Further, deep-learning techniques based on the convolution neural network are trained for learning rich feature hierarchies, in order to obtain the object of lying-pose human from objectness estimation, unlike the classical sliding-window algorithm. Eventually, employed pyramid mean-shift algorithm and rotation-angle recovery method to find position and direction of human lying-pose. The experimental results show that our method is rapid and efficient, and that it achieves state-of-the-art results with our XMULP dataset.

Keypoint descriptors play an indisputable role in modern vision, whether it is for image registration, content-based retrieval, 3D reconstruction, or object recognition. In that perspective, SIFT descriptor is the most widely used due to its robustness to scale, rotation, and perspective distortions as well as illumination changes. However, the processing effort and the amount of memory required by SIFT make it ill-suited for applications with limited memory and processing power. In this paper, we propose a novel keypoint descriptor called CBDF (Compressed Binary Discriminative Feature) designed to be accurate, memory efficient and fast at the same time. High speed and memory efficiency are made possible due to a binary descriptor obtained by aggregating gradient features. Local discriminative analysis (LDA) is used to make sure the aggregated feature leads to a discriminative descriptor. Accuracy is also enforced by the use of scale, rotation, and illumination invariant features. The CBDF descriptor is stored on a 32-byte vector (which is one order of magnitude smaller than the usual 512-byte vector for SIFT) while matching is done with the Hamming distance. Extensive evaluations on various benchmark datasets show that CBDF is not only faster and generally more accurate than SIFT and SURF, but also more accurate than recent descriptors such as ORB, BRISK and BRIEF.

A novel perspective invariant image matching framework is proposed in this paper, noted as Perspective-Invariant Binary Robust Independent Elementary Features (PBRIEF). First, we use the homographic transformation to simulate the distortion between two corresponding patches around the feature points. Then, binary descriptors are constructed by comparing the intensity of sample points surrounding the feature location. We transform the location of the sample points with simulated homographic matrices. This operation is to ensure that the intensities which we compared are the realistic corresponding pixels between two image patches. Since the exact perspective transform matrix is unknown, an Adaptive Particle Swarm Optimization (APSO) algorithm-based iterative procedure is proposed to estimate the real transformation angles. Experimental results obtained on five different datasets show that PBRIEF outperforms significantly the existing methods on images with large viewpoint difference. Moreover, the efficiency of our framework is also improved comparing with Affine-Scale Invariant Feature Transform (ASIFT).