Human Head Counting and Detection using Convnets

Now days, Detection of human head play an very important role in pedestrian counting. Machine learning is one platform, where human being can train a machine to act without being explicitly programmed and gives more accurate result, even when there is no enough data. Convolution neural network is one which works well for multimedia communication such as Text, Audio and Video. In this paper convnets play an important role in human head detection. In this paper it’s going to explain the less number of layers with more accuracy in the results with less time consuming

Human detection and face recognition in indoor environment to improve human-robot interaction in assistive and collaborative robots

Human detection in indoor environment is essential for Robots working together with humans in collaborative manufacturing environment. Similarly, Human detection is essential for service robots providing service with household chores or helping elderly population with different daily activities. Human detection can be achieved by Human Head detection, as head is the most discriminative part of human. Head detection method can be divided into three types: i) Method based on color mode; ii) Method based on template matching; and iii) Method based on contour detection. Method based on color mode is simple but is error prone. Method based on head template detects head in the image by searching for a template which is similar to head template. On the other hand, Method based on contour detection uses some information to describe head or head and shoulder information. The use of only one criteria may not be sufficient and accuracy of human head detection can be increased by combining the shape and color information. In this thesis, a method of human detection is proposed by combining the head shape and skin color (i.e., Combination of method based on Color mode and method based on Contour detection). Mainly, curvature criteria is used to segment out curves having similar curvature to find human head. Further, skin color is detected to localize face in image plane. A curve represents human head curve if only it has sufficient skin colored pixel in its closed proximity. Thus, by using color and human head curvature it was found that promising results could be obtained in human detection in indoor environment. iv After detecting humans in the surrounding, the next step for the robot could be to identify and recognize them. In this thesis, the use of Gabor filter response on nine points was investigated to identify eight different individuals. This suggests that the Gabor filter on nine points could be applied to identify people in small areas, for example home or small office with less individuals.Masters of Applied Science (M.A.Sc.) in Natural Resource Engineerin

DETECTION OF A HUMAN HEAD ON A LOW-QUALITY IMAGE AND ITS SOFTWARE IMPLEMENTATION

The paper considers the task solution of detection on two-dimensional images not only face, but head of a human regardless of the turn to the observer. Such task is also complicated by the fact that the image receiving at the input of the recognition algorithm may be noisy or captured in low light conditions. The minimum size of a person’s head in an image to be detected for is 10 × 10 pixels. In the course of development, a dataset was prepared containing over 1000 labelled images of classrooms at BSTU n.a. V.G. Shukhov. The markup was carried out using a segmentation software tool specially developed by the authors. Three architectures of convolutional neural networks were trained for human head detection task: a fully convolutional neural network (FCN) with clustering, the Faster R-CNN architecture and the Mask R-CNN architecture. The third architecture works more than ten times slower than the first one, but it almost does not give false positives and has the precision and recall of head detection over 90% on both test and training samples. The Faster R-CNN architecture gives worse accuracy than Mask R-CNN, but it gives fewer false positives than FCN with clustering. Based on Mask R-CNN authors have developed software for human head detection on a lowquality image. It is two-level web-service with client and server modules. This software is used to detect and count people in the premises. The developed software works with IP cameras, which ensures its scalability for different practical computer vision applications

Detecting human heads with their orientations

We propose a two-step method for detecting human heads with their orientations. In the first step, the method employs an ellipse as the contour model of human-head appearances to deal with wide variety of appearances. Our method then evaluates the ellipse to detect possible human heads. In the second step, on the other hand, our method focuses on features inside the ellipse, such as eyes, the mouth or cheeks, to model facial components. The method evaluates not only such components themselves but also their geometric configuration to eliminate false positives in the first step and, at the same time, to estimate face orientations. Our intensive experiments show that our method can correctly and stably detect human heads with their orientations

Delamination in fiberglass pre-impregnated laminated composites from ultrasonic a-scan signal using artificial intelligence

Impact-induced delamination (IID) in fiberglass pre-impregnated laminated composites (FGLC) is an important failure mode. Besides affected the material strength and structural reliability, this failure mode normally present minor damage on the surface but the internal damage may extensive. Existing detection method using static and dynamic load response have limitations that are considered static based monitoring and require the sensor to be attached to the test specimen surface. This technique is not suitable as the damage caused by the impact normally occurred by accident at random location. Thus, detection and classification of IID using artificial neural network from ultrasonic signal has great potential to be applied, but no attempt has been made to detect and classify this failure mode in FGLC material. The classification of delamination against impact not only applicable as prediction tool to characterise the delamination, it also can be used as reference during inspecting the FGLC under specific conditions. In this study, the potential of using ultrasonic immersion testing for detecting the IID in FGLC type 7781 E-Glass fabric is studied. Several findings and development have been achieved in this study such as the relationship between delamination area and the increasing of an impact energy, where the rate is between 23 to 45 percent. Besides, it was found that the diameter of the impact damage is directly increase with the increasing of the impact energy in the range of 21 until 46 percent while for the impact damage area is between 24 until 42 percent. In addition, the dynamic segmentation algorithm has been successfully developed in this study to automatically segment the A-scan signal with regardless the xxi variation of gap distance between transducer and specimen surface. Based on the ultrasonic inspection result, it was found that the delamination is extend internally up to 35.90 percent and the average percentage different of the measurement result which is taken from DT and NDT is just 4.72 percent and acceptable. Since the achieved classification result is highly accurate, which is exceeded 99.29 percent, it can be concluded that the selected features for the classification input is successful and the use of artificial neural network from ultrasonic A-scan signal has shown its applicability to classify the different type of the impact-induced delamination in FGLC plate

Detection of head position using chain code algorithm

Nowadays, autonomous vision-based system has been applied to handle human job that reliability and efficiency of any intelligent system gain improvement and enhancement. Human head detection is the first step of an autonomous human recognition system. This thesis focuses on a method to recognize and detect a human at the surveillance or highlighted area boundary based their head beside; a simulation system of head detection was developed using image processing. The main contribution of this thesis is it contributes an algorithm of head recognition and detecting which based on image segmentation, Prewitt edge detection and Chain Code algorithm. Static or still images are used as input data for simulation process. The use of Median Filter (MF) method for preprocessing stage is studied and implemented to make low noise for good signification in an image. Prewitt edge detecting (PED) has been applied to present boundary of features in the images in early stage. The image converted to binary image using Threshold Coding (TC) for difference between boundary and background. Features from the image are train using the Chain Code Algorithm (CCA) to do recognition of the crux human head then do detecting process. The low of complexity in mathematical equation is the factor of chosen this method, compares other techniques. Two environments have been applied to demonstrate the performance of the system, a person or more was detected for texture background and untextured background. The anahsis. design and development of simulation system are done in Visual C ^ . All the methods have been tested on image data and the experimental results have demonstrated a robust system

Assessment of algorithms for mitosis detection in breast cancer histopathology images

The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers. However, mitosis counting is laborious, subjective and may suffer from low inter-observer agreement. With the wider acceptance of whole slide images in pathology labs, automatic image analysis has been proposed as a potential solution for these issues. In this paper, the results from the Assessment of Mitosis Detection Algorithms 2013 (AMIDA13) challenge are described. The challenge was based on a data set consisting of 12 training and 11 testing subjects, with more than one thousand annotated mitotic figures by multiple observers. Short descriptions and results from the evaluation of eleven methods are presented. The top performing method has an error rate that is comparable to the inter-observer agreement among pathologists