The increasing global crime rate, coupled with substantial human and property losses, highlights the limitations of traditional surveillance methods in promptly detecting diverse and unexpected acts of violence. Addressing this pressing need for automatic violence detection, we leverage Machine Learning to detect and categorize violent events in video streams. This paper introduces a comprehensive framework for violence detection and classification, employing Supervised Learning for both binary and multi-class violence classification. The detection model relies on 3D Convolutional Neural Networks, while the classification model utilizes the separable convolutional 3D model for feature extraction and bidirectional LSTM for temporal processing. Training is conducted on a diverse customized datasets with frame-level annotations, incorporating videos from surveillance cameras, human recordings, hockey fight, sohas and wvd dataset across various platforms. Additionally, a camera module integrated with raspberry pi is used to capture live video feed, which is sent to the ML model for processing. Thus, demonstrating improved performance in terms of computational resource efficiency and accuracy.

Abstract-- The inspection of confined critical infrastructure such as attics or crawlspaces is challenging for human operators due to insufficient task space, limited visibility, and the presence of hazardous materials. This paper introduces a prototype of PARIS (Precision Application Robot for Inaccessible Spaces): a use-inspired teleoperated mobile robot manipulator system that was conceived, developed, and tested for--and selected as a Phase I winner of--the U.S. Department of Energy's E-ROBOT Prize. To improve the thermal efficiency of buildings, the PARIS platform supports: 1) teleoperated mapping and navigation, enabling the human operator to explore compact spaces; 2) inspection and sensing, facilitating the identification and localization of under-insulated areas; and 3) air-sealing targeted gaps and cracks through which thermal energy is lost. The resulting versatile platform can also be tailored for targeted application of treatments and remediation in constrained spaces. Approximately 75% of the world's greenhouse gas (GHG) emissions result from the cumulative energy sector [1].

Project Aria (Engel et al., 2023), Meta's all-day These models exhibit an unprecedented ability wearable AR glasses developed as data collection to understand and generate human-like language, tools for spatial computing. While Project Aria process visual and auditory information, and interpret aims to shift computing paradigms by blending digital 3D spatial environments (Zhao et al., 2023; interactions into the 3D world through spatial Yin et al., 2023; Engel et al., 2023). However, computing, Lucia extends these ideas by emphasizing as we push the boundaries of AI, a new frontier the temporal dimension. It prioritizes the emerges: Temporal Computing--the understanding continuous capture and intelligent interpretation of and utilization of time to construct contextual user activities over time while enhancing practical memory that enhances human cognition. This evolution usability: Lucia creates a device that not only has paved the way for devices that are not records but also understands and provides insightful only intelligent but also temporally aware, deeply responses based on the user's temporal expe-1

This article aims to cover pupillography, and its potential use in a number of ophthalmological diagnostic applications in biomedical space. With the ever-increasing incorporation of technology within our daily lives and an ever-growing active research into smart devices and technologies, we try to make a case for a health ecosystem that revolves around continuous eye monitoring. We tend to summarize the design constraints & requirements for an IoT-based continuous pupil detection system, with an attempt at developing a pipeline for wearable pupillographic device, while comparing two compact mini-camera modules currently available in the market. We use a light algorithm that can be directly adopted to current micro-controllers, and share our results for different lighting conditions, and scenarios. Lastly, we present our findings, along with an analysis on the challenges faced and a way ahead towards successfully building this ecosystem.

In this survey we are focusing on utilizing drone-based systems for the detection of individuals, particularly by identifying human screams and other distress signals. This study has significant relevance in post-disaster scenarios, including events such as earthquakes, hurricanes, military conflicts, wildfires, and more. These drones are capable of hovering over disaster-stricken areas that may be challenging for rescue teams to access directly. Unmanned aerial vehicles (UAVs), commonly referred to as drones, are frequently deployed for search-and-rescue missions during disaster situations. Typically, drones capture aerial images to assess structural damage and identify the extent of the disaster. They also employ thermal imaging technology to detect body heat signatures, which can help locate individuals. In some cases, larger drones are used to deliver essential supplies to people stranded in isolated disaster-stricken areas. In our discussions, we delve into the unique challenges associated with locating humans through aerial acoustics. The auditory system must distinguish between human cries and sounds that occur naturally, such as animal calls and wind. Additionally, it should be capable of recognizing distinct patterns related to signals like shouting, clapping, or other ways in which people attempt to signal rescue teams. To tackle this challenge, one solution involves harnessing artificial intelligence (AI) to analyze sound frequencies and identify common audio signatures. Deep learning-based networks, such as convolutional neural networks (CNNs), can be trained using these signatures to filter out noise generated by drone motors and other environmental factors. Furthermore, employing signal processing techniques like the direction of arrival (DOA) based on microphone array signals can enhance the precision of tracking the source of human noises.

State estimation is a critical foundational module in robotics applications, where robustness and performance are paramount. Although in recent years, many works have been focusing on improving one of the most widely adopted state estimation methods, visual inertial odometry (VIO), by incorporating multiple cameras, these efforts predominantly address synchronous camera systems. Asynchronous cameras, which offer simpler hardware configurations and enhanced resilience, have been largely overlooked. To fill this gap, this paper presents VINS-Multi, a novel multi-camera-IMU state estimator for asynchronous cameras. The estimator comprises parallel front ends, a front end coordinator, and a back end optimization module capable of handling asynchronous input frames. It utilizes the frames effectively through a dynamic feature number allocation and a frame priority coordination strategy. The proposed estimator is integrated into a customized quadrotor platform and tested in multiple realistic and challenging scenarios to validate its practicality. Additionally, comprehensive benchmark results are provided to showcase the robustness and superior performance of the proposed estimator.

The "Pro" moniker gets thrown around a lot on gadgets that are, at best, built for hobbyists with some disposable income. The original Mavic 3 was already a fantastic drone, but the newest variation packs an entire film studio into a small, flying package (and a high price to match its power). At $4,799, this drone is not cheap, but you can capture high-quality aerial shots! This is just not something that's really feasible without spending tons more. The most stark change in the Pro Cine variant of the Mavic 3 is its triple-camera array.

Remote patient monitoring is now a key application in medical spaces where cameras and AI are revolutionizing the delivery of care. This article will thus discuss how the two technologies work together to make life easier for patients and caregivers. The adoption of artificial intelligence is on the rise across all sectors. Though current AI cannot compete with the cognitive ability of the human brain, it has already started to dominate when it comes to performing mundane as well as intelligent tasks – and the medical field is not an exception to this. It has been captivating to see new and emerging applications and use cases where AI works in harmony with other technologies to enhance human experiences.

DJI's Mavic 3 created early buzz when a leak suggested it would have a large 4/3 sensor and dual camera system, along with an incredible 46 minutes of range. However, potential buyers were also shocked to learn that it has a $2,200 starting price, compared to $1,449 for the Mavic 2 Pro. And that goes way up to $5,000 if you want advanced features like ProRes HQ video. Early footage shows that the camera is indeed impressive and the 50 percent extra flight time is extremely useful. Buyers have also complained, though, about the price, overly basic Fly app and features like ActiveTrack 5 that won't be available until a January 2022 update.

On paper, the iPhone 13 and 13 mini aren't much to get excited about. Apple's subtle refinement on the iPhone 12 models will be familiar if you've paid attention to developments in the Android world. Some of the changes are impressive, like bringing the iPhone 12 Pro Max's excellent camera hardware to smaller phones and lower price points. Others, like a slightly smaller notch, bigger batteries, brighter displays, faster chips and expanded 5G support feel incremental. Still, they add up to make the iPhone 13 mini and iPhone 13 feel like worthwhile upgrades, especially to those looking to upgrade from older iPhones.