Taxonomy of Object Detection methods: A Survey

Object detection (OD) within a video is one of the relevant and critical research areas in the computer vision field. Due to the widespread of Artificial Intelligence, the basic principle in real life nowadays and its exponential growth predicted in the epochs to come, it will transmute the public. Object Detection has been extensively implemented in several areas, including human-machine Interaction, autonomous vehicles, security with video surveillance, and various fields that will be mentioned further. However, this augmentation of OD tackles different challenges such as occlusion, illumination variation, object motion, without ignoring the real-time aspect that can be quite problematic. This paper also includes some methods of application to take into account these issues. These techniques are divided into five subcategories: Point Detection, segmentation, supervised classifier, optical flow, a background modeling. This survey decorticates various methods and techniques used in object detection, as well as application domains and the problems faced. Our study discusses the cruciality of deep learning algorithms and their efficiency on future improvement in object detection topics within video sequences.

Research on Real-Time Face Key Point Detection Algorithm Based on Attention Mechanism

The existing face detection methods were affected by the network model structure used. Most of the face recognition methods had low recognition rate of face key point features due to many parameters and large amount of calculation. In order to improve the recognition accuracy and detection speed of face key points, a real-time face key point detection algorithm based on attention mechanism was proposed in this paper. Due to the multiscale characteristics of face key point features, the deep convolution network model was adopted, the attention module was added to the VGG network structure, the feature enhancement module and feature fusion module were combined to improve the shallow feature representation ability of VGG, and the cascade attention mechanism was used to improve the deep feature representation ability. Experiments showed that the proposed algorithm not only can effectively realize face key point recognition but also has better recognition accuracy and detection speed than other similar methods. This method can provide some theoretical basis and technical support for face detection in complex environment.

Core point pixel-level localization by fingerprint features in spatial domain

<abstract> <p>Singular point detection is a primary step in fingerprint recognition, especially for fingerprint alignment and classification. But in present there are still some problems and challenges such as more false-positive singular points or inaccurate reference point localization. This paper proposes an accurate core point localization method based on spatial domain features of fingerprint images from a completely different viewpoint to improve the fingerprint core point displacement problem of singular point detection. The method first defines new fingerprint features, called furcation and confluence, to represent specific ridge/valley distribution in a core point area, and uses them to extract the innermost Curve of ridges. The summit of this Curve is regarded as the localization result. Furthermore, an approach for removing false Furcation and Confluence based on their correlations is developed to enhance the method robustness. Experimental results show that the proposed method achieves satisfactory core localization accuracy in a large number of samples.</p> </abstract>

Array model of shock pressure sensor for shooting point detection

The array model of double-T shock pressure sensor is established. Shock wave is produced by a supersonic object in the air. Pressure is produced in the process of shock wave transmission. Different shock pressure sensors have different time to receive the pressure signal. In this paper, the shooting point calculation model and the finite element model of the double T-shaped array method are established. The simulation experiment is carried out. The law of shock wave propagation is verified. The model can be used to calculate the coordinates of shooting point quickly. This method is suitable for small angle oblique fire location problem, and improves the detection accuracy of shooting point.

A Deep Learning Based System for the Detection of Human Violence in Video Data

The number of security cameras positioned within the surrounding area has expanded, increasing the demand for automatic activity recognition systems. In addition to offline assessment and the issuance of an ongoing alarm in the case of aberrant behaviour, automatic activity detection systems can be employed in conjunction with human operators. In the proposed research framework, an ensemble of Mask Region-based Convolutional Neural Networks for key-point detection scheme, and LSTM based Recurrent Neural Network is used to create a deep neural network model (Mask RCNN) for recognizing violent activities (i.e. kicking, punching, etc.) of a single person. First of all, the key-points locations and ground-truth masks of humans in an image are selected using the selected region; the temporal information is extracted. Experimental results show that the ensemble model outperforms individual models. The proposed technique has a reasonable accuracy rate of 77.4 percent, 95.7 percent, and 88.2 percent, respectively, on the Weizmann, KTH, and our custom datasets. As the proposed effort applies to industry and in terms of security, it is beneficial to society.