In this paper, we introduce EgoSim, a novel simulator of body-worn cameras that generates realistic egocentric renderings from multiple perspectives across a wearer's body.

Research on egocentric tasks in computer vision has mostly focused on head-mounted cameras, such as fisheye cameras or embedded cameras inside immersive headsets.We argue that the increasing miniaturization of optical sensors will lead to the prolific integration of cameras into many more body-worn devices at various locations.This will bring fresh perspectives to established tasks in computer vision and benefit key areas such as human motion tracking, body pose estimation, or action recognition---particularly for the lower body, which is typically occluded.In this paper, we introduce EgoSim, a novel simulator of body-worn cameras that generates realistic egocentric renderings from multiple perspectives across a wearer's body.A key feature of EgoSim is its use of real motion capture data to render motion artifacts, which are especially noticeable with arm- or leg-worn cameras.In addition, we introduce MultiEgoView, a dataset of egocentric footage from six body-worn cameras and ground-truth full-body 3D poses during several activities:119 hours of data are derived from AMASS motion sequences in four high-fidelity virtual environments, which we augment with 5 hours of real-world motion data from 13 participants using six GoPro cameras and 3D body pose references from an Xsens motion capture suit.We demonstrate EgoSim's effectiveness by training an end-to-end video-only 3D pose estimation network.Analyzing its domain gap, we show that our dataset and simulator substantially aid training for inference on real-world data.EgoSim code & MultiEgoView dataset: https://siplab.org/projects/EgoSim

Research on egocentric tasks in computer vision has mostly focused on head-mounted cameras, such as fisheye cameras or embedded cameras inside immersive headsets. We argue that the increasing miniaturization of optical sensors will lead to the prolific integration of cameras into many more body-worn devices at various locations. This will bring fresh perspectives to established tasks in computer vision and benefit key areas such as human motion tracking, body pose estimation, or action recognition -- particularly for the lower body, which is typically occluded. In this paper, we introduce EgoSim, a novel simulator of body-worn cameras that generates realistic egocentric renderings from multiple perspectives across a wearer's body. A key feature of EgoSim is its use of real motion capture data to render motion artifacts, which are especially noticeable with arm- or leg-worn cameras. In addition, we introduce MultiEgoView, a dataset of egocentric footage from six body-worn cameras and ground-truth full-body 3D poses during several activities: 119 hours of data are derived from AMASS motion sequences in four high-fidelity virtual environments, which we augment with 5 hours of real-world motion data from 13 participants using six GoPro cameras and 3D body pose references from an Xsens motion capture suit. We demonstrate EgoSim's effectiveness by training an end-to-end video-only 3D pose estimation network. Analyzing its domain gap, we show that our dataset and simulator substantially aid training for inference on real-world data. EgoSim code & MultiEgoView dataset: https://siplab.org/projects/EgoSim

Several police departments nationwide are debuting artificial intelligence that writes officers' incident reports for them, and although the software could cause issues in court, an expert says, the technology could be a boon for law enforcement. Oklahoma City's police department was among the first to experiment with Draft One, an AI-powered software that analyzes police body-worn camera audio and radio transmissions to write police reports that can later be used to justify criminal charges and as evidence in court. Since The Associated Press detailed the software and its use by the department in a late August article, the department told Fox News Digital that it has put the program on hold. "The use of the AI report writing has been put on hold, so we will pass on speaking about it at this time," Capt. Valerie Littlejohn wrote via email.

On any given day, Los Angeles police officers record roughly 8,000 interactions with the public on body-worn cameras. Most of the footage goes unseen. The city spent millions on the cameras to help provide transparency and accountability, but LAPD officials say they don't have enough personnel to monitor the countless hours of recordings. The department has also struggled to keep tabs on whether officers are turning off their cameras in violation of department rules -- as members of a disbanded gang unit from the Mission division are suspected of doing in order to cover up thefts, unlawful searches, and other alleged misconduct. A recent internal report suggested lapses in body-cam activation are more widespread than the department has previously let on, and that its system for auditing compliance falls short.

Video evidence, from dash and body-worn cameras, surveillance systems and smartphones, has given rise to a new dimension of police investigations. There is often so much video evidence that the investigator is overwhelmed by the task of culling out the irrelevant segments to focus on those that provide genuine insight to the incident. Video analytics powered by artificial intelligence (AI) simplifies and accelerates that process. Investigators may be forced to watch many hours of uneventful video, looking for the brief segment that contains evidence. A similar problem is when someone is assigned to monitor multiple live feeds of surveillance video, and is to send out an alert when something noteworthy is observed.

In the last few years, many law enforcement agencies have adopted body worn cameras. In this blog post, I will provide some background on what is driving the growth and will talk about how AI can help law enforcement agencies with the processing of videos captured by body-worn cameras. A body worn camera is a wearable audio, video or photographic recording system. Law enforcement agencies are not the only consumers of body-worn cameras. Other consumers include journalists, medical professionals, athletes, and so on.

Criminals looking for a quiet pint suddenly found themselves collared when cops used facial recognition technology to catch thirsty crooks at a Chinese beer festival. Twenty-five wanted individuals were arrested when they tipped up to sample the offerings at the annual bash in Qingdao--home to China's most famous brew. Those snared included one man who had been on the run for ten years, only to be undone by his hankering for a lager. Eighteen cameras installed at four entrances to the festival identified each of the suspects in under one second, Qingdao police said this week. According to Qingdao authorities, the system has a 98.1 accuracy rate and sounds an alarm if a subject's face is found in the police database.

Police officers could soon be wearing body-mounted cameras programmed to spot criminals and missing people in real-time, using artificial intelligence. The cameras, built by Motorola and similar to those already used by some US police forces to record an officer's point of view, could also help find missing objects like a stolen car, thanks to machine learning. A prototype of the AI camera is already being developed by Motorola and Neurala, a deep learning startup based in Boston, Massachusetts that recently added its software to drone cameras to help track poachers in Africa. The smart camera will learn while it is used and "automatically search for persons or objects of interest, significantly reducing the time and effort required to find a missing child or suspicious object in environments that are often crowded or chaotic," Motorola and Neurala said in a joint statement. "We see powerful potential for artificial intelligence to improve safety and efficiency for our customers, which in turn helps create safer communities," said Paul Steinberg, chief technology officer of Motorola Solutions.