Identification Of Human Targets Research Articles

Computational identification of human targets of mitragynine-the main active compound of Mitragyna speciosa

Computational identification of human targets of mitragynine-the main active compound of Mitragyna speciosa

The risks of autonomous weapons: An analysis centred on the rights of persons with disabilities

AbstractAutonomous weapons systems have been the subject of heated debate since 2010, when Philip Alston, then Special Rapporteur on Extrajudicial, Summary, or Arbitrary Executions, brought the issue to the international spotlight in his interim report to the United Nations (UN) General Assembly 65th Session. Alston affirmed that “automated technologies are becoming increasingly sophisticated, and artificial intelligence reasoning and decision-making abilities are actively being researched and receive significant funding. States’ militaries and defence industry developers are working to develop ‘fully autonomous capability’, such that technological advances in artificial intelligence will enable unmanned aerial vehicles to make and execute complex decisions, including the identification of human targets and the ability to kill them.”1 Later, in 2013, Christof Heyns, who was Special Rapporteur for Extrajudicial, Summary or Arbitrary Executions at the time, published a report that elaborated further on the issues raised by what he called “lethal autonomous robotics”.2 Following a recommendation by Advisory Board on Disarmament Matters at the UN General Assembly 68th Session, the Convention on Prohibitions or Restrictions on the Use of Certain Conventional Weapons Which May Be Deemed to Be Excessively Injurious or to Have Indiscriminate Effects, as amended on 21 December 2021, started discussing autonomous weapons systems in 2014. Then, the Group of Governmental Experts on Emerging Technologies in the Area of Lethal Autonomous Weapons Systems (GGE on LAWS)3 was created in 2016 to focus on this issue.4 While the group has kept meeting since then, no clear steps have been taken yet towards a normative framework on autonomous weapons as of September 2022.In all these years, persons with disabilities – including conflict survivors – have not been included in discussions, nor has the disability perspective been reflected in international debate on autonomous weapons. Only recently has there been any effort to consider the rights of persons with disabilities when examining ethical questions related to artificial intelligence (AI). In this article, we will examine how and why autonomous weapons have a disproportionate impact on persons with disabilities, because of the discrimination that results from a combination of factors such as bias in AI, bias in the military and the police, barriers to justice and humanitarian assistance in situations of armed conflict, and the lack of consultation and participation of persons with disabilities and their representative organizations on issues related to autonomy in weapons systems.

Computer Vision for Human-Computer Interaction Using Noninvasive Technology

Computer vision is a significant component of human-computer interaction (HCI) processes in interactive control systems. In general, the interaction between humans and computers relies on the flexibility of the interactive visualization system. Electromyography (EMG) is a bioelectric signal used in HCI that can be captured noninvasively by placing electrodes on the human hand. Due to the impact of complex background, accurate recognition and analysis of human motion in real-time multitarget scenarios are considered challenging in HCI. Further, EMG signals of human hand motions are exceedingly nonlinear, and it is important to utilize a dynamic approach to address the noise problem in EMG signals. Hence, in this paper, the Optimized Noninvasive Human-Computer Interaction (ONIHCI) model has been proposed to predict human motion recognition. Average Intrinsic Mode Function (AIMF) has been used to reduce the noise factor in EMG signals. Furthermore, this paper introduces spatial thermographic imaging to overcome the conventional sensor problem, such as gesture recognition and human target identification in multitarget scenarios. The human motion behavior in spatial thermographic images is examined by target trajectory, and body movement kinematics is employed to classify human targets and objects. The experimental findings demonstrate that the proposed method reduces noise by 7.2% and improves accuracy by 97.2% in human motion recognition and human target identification.

Visual Perception for Multiple Human–Robot Interaction From Motion Behavior

Visual perception is an important component for human–robot interaction processes in robotic systems. Interaction between humans and robots depends on the reliability of the robotic vision systems. The variation of camera sensors and the capability of these sensors to detect many types of sensory inputs improve the visual perception. The analysis of activities, motions, skills, and behaviors of humans and robots have been addressed by utilizing the heat signatures of the human body. The human motion behavior is analyzed by body movement kinematics, and the trajectory of the target is used to identify the objects and the human target in the omnidirectional (O-D) thermal images. The process of human target identification and gesture recognition by traditional sensors have problem for multitarget scenarios since these sensors may not keep all targets in their narrow field of view (FOV) at the same time. O-D thermal view increases the robots’ line-of-sights and ability to obtain better perception in the absence of light. The human target is informed of its position, surrounding objects and any other human targets in its proximity so that humans with limited vision or vision disability can be assisted to improve their ability in their environment. The proposed method helps to identify the human targets in a wide FOV and light independent conditions to assist the human target and improve the human–robot and robot–robot interactions. The experimental results show that the identification of the human targets is achieved with a high accuracy.

Vital-SAR-Imaging With a Drone-Based Hybrid Radar System

Full-coverage synthetic-aperture radar (SAR) imaging and human target identification in complex scenes are very important for security and rescue applications. In this paper, a drone-based vital-SAR-imaging hybrid radar system is proposed to realize targets’ imaging and human identification simultaneously. Carried by an operational custom-built drone with a designed S-shaped trajectory, 2-D SAR images of the entire region could be obtained through the frequency-modulated continuous-wave mode, and the “vital Doppler” effect of human target can be searched through the Doppler mode. Then, the vital-SAR-imaging could be achieved. Several experiments including cart-based SAR imaging with three trajectories, cart-based vital-SAR-imaging, and drone-based multitrajectory vital-SAR-imaging were carried out to reveal the capability and distinct operational features of the proposed system. Experimental results demonstrate that the system has the ability to obtain SAR imaging results in different scenes with multiple trajectories, and the human target could be effectively distinguished from other stationary objects. The results show that the lowest human target location error is 0.06 m in a $4\,\,\text {m} \times 9$ m indoor region, which illustrates the great potential for search and rescue applications.

Fusion of Different Height Pyroelectric Infrared Sensors for Person Identification

Due to the instability and poor identification ability of a single pyroelectric infrared (PIR) detector for human target identification, this paper presents a PIR detection identification system that can collect thermal infrared features from different parts of human targets through multiple PIR sensors for the human identification. First, fast Fourier transform, short-time Fourier transform, and wavelet packet transform algorithms are adopted to extract thermal infrared features of human targets. Then, the canonical correlation analysis algorithm is used to fuse different algorithm features in the feature layer. Finally, using the support vector machine to classify the human targets. In the decision-making layer, the Dempster/Shafer evidence theory is adopted to optimize the recognition results from different PIR sensors that locate at different height positions. Extensive experimental results demonstrate that the fusion of feature layer data could improve the average recognition rate of the human target with closer distance from the single sensor. In addition, the fusion of decision-making layer could improve the recognition ability of the identification system as well. When the detection distance is 6 m, the correct recognition rate of fusion system is still reached 88.75%. Compared with the system using a single sensor, the recognition rate is increased by an average of 22.67%.

Fusion of Multiple Pyroelectric Characteristics for Human Body Identification

Due to instability and poor identification ability of single pyroelectric infrared (PIR) detector for human target identification, this paper proposes a new approach to fuse the information collected from multiple PIR sensors for human identification. Firstly, Fast Fourier Transform (FFT), Short Time Fourier Transform (STFT), Wavelet Transform (WT) and Wavelet Packet Transform (WPT) are adopted to extract features of the human body, which can be achieved by single PIR sensor. Then, we apply Principal Component Analysis (PCA) and Support Vector Machine (SVM) to reduce the characteristic dimensions and to classify the human targets, respectively. Finally, Fuzzy Comprehensive Evaluation (FCE) is utilized to fuse recognition results from multiple PIR sensors to finalize human identification. The pyroelectric characteristics under scenarios with different people and/or different paths are analyzed by various experiments, and the recognition results with/without fusion procedure are also shown and compared. The experimental results demonstrate our scheme has improved efficiency for human identification.