Detection And Tracking Of Moving Objects using Particle Filter

Motion detection is the first essential process in the extraction of information regarding moving objects and makes use of stabilization in functional areas such as tracking, classification, recognition, and so on. In this paper, high - quality moving object detection is determined by using nonparametric modeling. The background is modeled by using the combination of chromaticity and gradients; it reduces the influence of shadows and reflected light. The foreground model combines this information and spatial information. Particle filter is introduced update the spatial information. The detection results produced by the partic le filter is analysed through visual inspection and for accuracy, along with comparisons to the results produced by other state - of - the - art methods

Multispectral persistent surveillance

The goal of a successful surveillance system to achieve persistence is to track everything that moves, all of the time, over the entire area of interest. The thrust of this thesis is to identify and improve upon the motion detection and object association aspect of this challenge by adding spectral information to the equation. Traditional motion detection and tracking systems rely primarily on single-band grayscale video, while more current research has focused on sensor fusion, specifically combining visible and IR data sources. A further challenge in covering an entire area of responsibility (AOR) is a limited sensor field of view, which can be overcome by either adding more sensors or multi-tasking a single sensor over multiple areas at a reduced frame rate. As an essential tool for sensor design and mission development, a trade study was conducted to measure the potential advantages of adding spectral bands of information in a single sensor with the intention of reducing sensor frame rates. Thus, traditional motion detection and object association algorithms were modified to evaluate system performance using five spectral bands (visible through thermal IR), while adjusting frame rate as a second variable. The goal of this research was to produce an evaluation of system performance as a function of the number of bands and frame rate. As such, performance surfaces were generated to assess relative performance as a function of the number of bands and frame rate

Background Stabilization And Motion Detection In Launch Pad Video Monitoring

Automatic detection of moving objects in video sequences is a widely researched topic with application in surveillance operations. Methods based on background cancellation by frame differencing are extremely common. However this process becomes much more complicated when the background is not completely stable due to camera motion. This thesis considers a space application where surveillance cameras around a shuttle launch site are used to detect any debris from the shuttle. The ground shake due to the impact of the launch causes the background to be shaky. We stabilize the background by translation of each frame, the optimum translation being determined by minimizing the energy difference between consecutive frames. This process is optimized by using a sub-image instead of the whole frame, the sub-image being chosen by taking an edge detection plot of the background and choosing the area with greatest density of edges as the sub-image of interest. The stabilized sequence is then processed by taking the difference between consecutive frames and marking areas with high intensity as the areas where motion is taking place. The residual noise from the background stabilization part is filtered out by masking the areas where the background has edges, as these areas have the highest probability of false alarms due to background motion

An a contrario decision framework for motion detection

Motion detection aims at discriminating between moving objects and a static environment. This task can be seen as the grouping of local motion observations into moving objects. The framework we propose is derived from a perceptual grouping principle, namely the Helmholtz principle. It consists in defining an image model in the absence of moving objects instead of modeling the moving objects. This prevents from any complex model design while enforcing the generality of the approach, since there is no prior to specify on the objects to be detected. Detections are then said to be performed a contrario moving regions appear as low probability events in the "no motion" or a contrario model. The modeling framework induced by this approach is compact and handy, since it is simply built on independant identically distributed random variables. Furthermore, computing automatic detection thresholds and attaching a confidence level to each detected moving region is possible through the probalistic setting of the framework. The resulting detection algorithm is thus truly generic and avoids parameter tuning. The method performance is assessed on various real image sequences

A neurobiological and computational analysis of target discrimination in visual clutter by the insect visual system.

Some insects have the capability to detect and track small moving objects, often against cluttered moving backgrounds. Determining how this task is performed is an intriguing challenge, both from a physiological and computational perspective. Previous research has characterized higher-order neurons within the fly brain known as 'small target motion detectors‘ (STMD) that respond selectively to targets, even within complex moving surrounds. Interestingly, these cells still respond robustly when the velocity of the target is matched to the velocity of the background (i.e. with no relative motion cues). We performed intracellular recordings from intermediate-order neurons in the fly visual system (the medulla). These full-wave rectifying, transient cells (RTC) reveal independent adaptation to luminance changes of opposite signs (suggesting separate 'on‘ and 'off‘ channels) and fast adaptive temporal mechanisms (as seen in some previously described cell types). We show, via electrophysiological experiments, that the RTC is temporally responsive to rapidly changing stimuli and is well suited to serving an important function in a proposed target-detecting pathway. To model this target discrimination, we use high dynamic range (HDR) natural images to represent 'real-world‘ luminance values that serve as inputs to a biomimetic representation of photoreceptor processing. Adaptive spatiotemporal high-pass filtering (1st-order interneurons) shapes the transient 'edge-like‘ responses, useful for feature discrimination. Following this, a model for the RTC implements a nonlinear facilitation between the rapidly adapting, and independent polarity contrast channels, each with centre-surround antagonism. The recombination of the channels results in increased discrimination of small targets, of approximately the size of a single pixel, without the need for relative motion cues. This method of feature discrimination contrasts with traditional target and background motion-field computations. We show that our RTC-based target detection model is well matched to properties described for the higher-order STMD neurons, such as contrast sensitivity, height tuning and velocity tuning. The model output shows that the spatiotemporal profile of small targets is sufficiently rare within natural scene imagery to allow our highly nonlinear 'matched filter‘ to successfully detect many targets from the background. The model produces robust target discrimination across a biologically plausible range of target sizes and a range of velocities. We show that the model for small target motion detection is highly correlated to the velocity of the stimulus but not other background statistics, such as local brightness or local contrast, which normally influence target detection tasks. From an engineering perspective, we examine model elaborations for improved target discrimination via inhibitory interactions from correlation-type motion detectors, using a form of antagonism between our feature correlator and the more typical motion correlator. We also observe that a changing optimal threshold is highly correlated to the value of observer ego-motion. We present an elaborated target detection model that allows for implementation of a static optimal threshold, by scaling the target discrimination mechanism with a model-derived velocity estimation of ego-motion. Finally, we investigate the physiological relevance of this target discrimination model. We show that via very subtle image manipulation of the visual stimulus, our model accurately predicts dramatic changes in observed electrophysiological responses from STMD neurons.Thesis (Ph.D.) - University of Adelaide, School of Molecular and Biomedical Science, 200

Precise foreground detection algorithm using motion estimation, minima and maxima inside the foreground object

In this paper the precise foreground mask is obtained in a complex environment by applying simple and effective methods on a video sequence consisting of multi-colour and multiple foreground object environment. To detect moving objects we use a simple algorithm based on block-based motion estimation, which requires less computational time. To obtain a full and improved mask of the moving object, we use an opening-and-closing-by- reconstruction mechanism to identify the minima and maxima inside the foreground object by applying a set of morphological operations. This further enhances the outlines of foreground objects at various stages of image processing. Therefore, the algorithm does not require the knowledge of the background image. That is why it can be used in real world video sequences to detect the foreground in cases where we do not have a background model in advance. The comparative performance results demonstrate the effectiveness of the proposed algorithm.The Institute of Management Sciences Peshawar (http://imsciences.edu.pk/) through Higher Education Commission Islamabad, Pakistan (http://hec.gov.pk/)