Motion Detection by Microcontroller for Panning Cameras

Motion detection is the first essential process in the extraction of information regarding moving objects. The approaches based on background difference are the most used with fixed cameras to perform motion detection, because of the high quality of the achieved segmentation. However, real time requirements and high costs prevent most of the algorithms proposed in literature to exploit the background difference with panning cameras in real world applications. This paper presents a new algorithm to detect moving objects within a scene acquired by panning cameras. The algorithm for motion detection is implemented on a Raspberry Pi microcontroller, which enables the design and implementation of a low-cost monitoring system.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Wieldy Finger and Hand Motion Detection for Human Computer Interaction

We have developed a gesture based interface for human computer interaction under the research field of computer vision.Earlier system have used the costlier system devices to make an effective interaction with systems, instead we have worked on the web cam based gesture input system.Our goal was to propound lesser cost, wieldy, object detection technique using blobs for detection of fingers.And to give number of count of the same.In addition, we have also implemented the hand gesture recognition

Test report, earth orbiter teleoperator visual system evaluation program

Work carried out to identify human performance requirements for remotely manned system is reported. Specifically, an evaluation was made of the human visual system. Data cover distance estimation 4, solid target alignment 2, motion detection 1, and motion detection 2

Sensing motion using spectral and spatial analysis of WLAN RSSI

In this paper we present how motion sensing can be obtained just by observing the WLAN radio signal strength and its fluctuations. The temporal, spectral and spatial characteristics of WLAN signal are analyzed. Our analysis confirms our claim that ’signal strength from access points appear to jump around more vigorously when the device is moving compared to when it is still and the number of detectable access points vary considerably while the user is on the move’. Using this observation, we present a novel motion detection algorithm, Spectrally Spread Motion Detection (SpecSMD) based on the spectral analysis of WLAN signal’s RSSI. To benchmark the proposed algorithm, we used Spatially Spread Motion Detection (SpatSMD), which is inspired by the recent work of Sohn et al. Both algorithms were evaluated by carrying out extensive measurements in a diverse set of conditions (indoors in different buildings and outdoors - city center, parking lot, university campus etc.,) and tested against the same data sets. The 94% average classification accuracy of the proposed SpecSMD is outperforming the accuracy of SpatSMD (accuracy 87%). The motion detection algorithms presented in this paper provide ubiquitous methods for deriving the state of the user. The algorithms can be implemented and run on a commodity device with WLAN capability without the need of any additional hardware support

Independent Motion Detection with Event-driven Cameras

Unlike standard cameras that send intensity images at a constant frame rate, event-driven cameras asynchronously report pixel-level brightness changes, offering low latency and high temporal resolution (both in the order of micro-seconds). As such, they have great potential for fast and low power vision algorithms for robots. Visual tracking, for example, is easily achieved even for very fast stimuli, as only moving objects cause brightness changes. However, cameras mounted on a moving robot are typically non-stationary and the same tracking problem becomes confounded by background clutter events due to the robot ego-motion. In this paper, we propose a method for segmenting the motion of an independently moving object for event-driven cameras. Our method detects and tracks corners in the event stream and learns the statistics of their motion as a function of the robot's joint velocities when no independently moving objects are present. During robot operation, independently moving objects are identified by discrepancies between the predicted corner velocities from ego-motion and the measured corner velocities. We validate the algorithm on data collected from the neuromorphic iCub robot. We achieve a precision of ~ 90 % and show that the method is robust to changes in speed of both the head and the target.Comment: 7 pages, 6 figure

Dogs are not better than humans at detecting coherent motion

The ability to perceive motion is one of the main properties of the visual system. Sensitivity in detecting coherent motion has been thoroughly investigated in humans, where thresholds for motion detection are well below 10% of coherence, i.e. of the proportion of dots coherently moving in the same direction, among a background of randomly moving dots. Equally low thresholds have been found in other species, including monkeys, cats and seals. Given the lack of data from the domestic dog, we tested 5 adult dogs on a conditioned discrimination task with random dot displays. In addition, five adult humans were tested in the same condition for comparative purposes. The mean threshold for motion detection in our dogs was 42% of coherence, while that of humans was as low as 5%. Therefore, dogs have a much higher threshold of coherent motion detection than humans, and possibly also than phylogenetically closer species that have been tested in similar experimental conditions. Various factors, including the relative role of global and local motion processing and experience with the experimental stimuli may have contributed to this result. Overall, this finding questions the general claim on dogs' high performance in detecting motion

A STUDY OF HUMAN PILOTS' ABILITY TO DETECT ANGULAR MOTION WITH APPLICATION TO CONTROL OF SPACE RENDEZVOUS

Angular motion detection by human pilots and application to space rendezvous contro

Periodic Motion Detection and Estimation via Space-Time Sampling

A novel technique to detect and localize periodic movements in video is presented. The distinctive feature of the technique is that it requires neither feature tracking nor object segmentation. Intensity patterns along linear sample paths in space-time are used in estimation of period of object motion in a given sequence of frames. Sample paths are obtained by connecting (in space-time) sample points from regions of high motion magnitude in the first and last frames. Oscillations in intensity values are induced at time instants when an object intersects the sample path. The locations of peaks in intensity are determined by parameters of both cyclic object motion and orientation of the sample path with respect to object motion. The information about peaks is used in a least squares framework to obtain an initial estimate of these parameters. The estimate is further refined using the full intensity profile. The best estimate for the period of cyclic object motion is obtained by looking for consensus among estimates from many sample paths. The proposed technique is evaluated with synthetic videos where ground-truth is known, and with American Sign Language videos where the goal is to detect periodic hand motions.National Science Foundation (CNS-0202067, IIS-0308213, IIS-0329009); Office of Naval Research (N00014-03-1-0108

Web Camera Application For Motion Detection

Motion detection is the ability to recognize the presence of movements. There are many different ways to detect motion. The conventional way is by using either active sensor or passive sensor. The new method to detect motion is "vision motion detection". It is the artificial way of machine vision system compared to human's vision in detecting motion. Motion detection is the most important feature in digital video surveillance system. It gives the camera the capability to capture when needed rather than capture all the time and this leads to huge reduction in storage space. Alarm can also be triggered when unexpected motion is detected. This relieves the personnel in monitoring at all time.This thesis presents the design and implementation of a low cost security system. The system consists of only a web camera and a personal computer, which is incorporated with motion detection capability. The motion detection capability is using the concept of "motion detection by vision". Therefore no hardware sensors like active sensor and passive sensor are required. The motion detection capability provided in this system is derived from image subtraction method. This thesis project consists of three main stages, namely hardware setup, simulation and implementation. The first stage is setting up the system of which consists of PC and web camera. The web camera is only operable with the web camera driver installed in the PC. In the second stage, simulation done on the frame images using Matlab with Image Processing Toolbox as simulation tool to investigate the possibilities of motion detection algorithm on images captured by web camera. In the third stage, implementation process is done by coding the motion detection software using Microsoft Visual Basic 6.0. The algorithm that was successfully simulated is used as the reference for forming the working mechanism in creating motion detection software. This self-created motion detection software is then installed and run on the PC to function as a complete intelligent motion detection system