Essay On Object Detection

For activity 2, the frame is run through a thresholding function that creates a binary image based on the histogram levels that are similar to that of a stop sign. Any pixel that falls within the levels are then converted to a value of one and the rest are set to zero. The binary image is then put through two morphological operators, open and close. The open morphological operator gets rid of smaller clutters of pixels as defined by the parameters that are passed in to the function. The close morphological operator fills in clusters of pixels that are the shape of the parameter passed to the function. In this case, the shape is an octagon just like a stop sign. The last step is to run the binary image through blob analysis to get the ROIs that are of a specified minimum size. Once the ROIs have been found, they are passed into the cascade object detector for verification of

3.1 Multi-Layer Perceptron Regression . . . . . . . . . . . . . . . . . . . . . . . . 14

A series of experiments was performed on glass beads and natural clean dry sands under the objective of the current work of performing parametric studies. Therefore, new techniques and methods were utilized to predict the gradation of the natural cohesionless silica sands tested in addition to the conventional geotechnical laboratory experiments, which were carried out to predict the mechanical characteristics of such soils. Moreover, ideal laboratory simulations for the SPT were performed under several particular relative densities, loading conditions, and stress-strain controlled boundaries. Additionally, the obtained results from such series of experiments were stored in digital forms for further processing and analyses.

The Algorithm Based Object Recognitions and Tracking (ABORAT) system offers the best solution for implementing a surveillance system based on automated behavior analysis. Its ability to interface with off-the-shelf products means

Automated video surveillance is one of these applications that monitors people and vehicles in real time within a busy environment. The purpose of a surveillance system is to record properties and movements of objects in a given area and inform authorities in case of an incident ( Javed, & Shah, 2008). Automated video surveillance is very advantageous for the federal government of the United States because it can help detect possible terrorist attacks.

Front Camera for Near Vision: A camera mounted on the windshield deals with helping the car see objects directly before it. These incorporate the typical suspects-people on foot, and different drivers. This camera likewise recognizes and records data about street signs and movement lights, which is keenly translated by the car in assembled programming.

After reading the digital image into internal memory, the program uses cvThreshold function to transfer the colorful image into a grayscale one. Then according to the optimal variance, transfer the grayscale image to a binary image. After that, the program uses cvDilate and cvErode functions to do dilation and erosion operation. Thus, in order to improve the accuracy rate, cvSmooth function is used to smooth the edges of the image. Then program uses canny operator to detect and outline edges. At the end, circle hough transform is used to detect the circles in the images, which are the sign of human head. So the program

The outline of the stages of the image processing pipeline is; realign (affine), skull scripting, statistical edge detector (multi-scale, multi-modal), extract statistical features, use it to build SVM. The robustness of implementation will be tested with Pixel Correspondence Metric (PCM). The same pipeline will be followed for an unsupervised approach using leave one out (LOOCV). The filter size, parametric and non-parametric features like mean, variance, rank, U Mann Whitney test etc will be evaluated and an optimal performer will be selected.

The first way is fusing the standard camera data stream with that of a forward looking infrared (FLIR) system. FLIR systems have been used for years by the military to detect enemy combatants and vehicles via their heat signatures. The major advantage of this approach is that it works just as well, if not better, during the night. While a standard camera produces a large amount of low-light noise, infrared cameras can produce high resolution images which can be used for accurate obstacle detection. I plan to use the fused data for feature extraction with a suite of image processing methods, such as Canny edge detection. Finally, I plan to use the extracted features to classify them with common methods such as neural networks or SVMs. I expect this phase of my research to take one to one and a half years, because it relies on proven technology being used in a new

The picture of the vehicle whose number plate is to be distinguished is caught utilizing the advance camera. Next the number plate is removed by right of the bat changing over rgb image I.e., the caught picture to gray scale image with the specific goal to encourage the plate extraction, and increment the handling speed. Shading picture is produced by advance camera is changed over to dim scale picture utilizing. The essential

The street which runs in an East and West direction does not have any street lighting, with enough parking which can be used for any Sub-Rosa surveillance during the evening and daylight

We refer the interested reader to \cite{kaur2011survey,bedi2013various} for a review of image enhancement methods, to \cite{kaur2012comparative} for signal denoising methods, to \cite{854761,6248014} for region of interest detection, and to \cite{sagonas2013300,Zhang2014} for facial landmarks detection. In addition, we want to note that understanding this paper requires basic knowledge of machine-learning concepts such as feature (i.e., a measurable property of an object), feature vector (i.e., n-dimensional vector of numerical features), classifier's accuracy, and other performance evaluation techniques. A simple, yet comprehensive, explanation of these concepts can be found in

The use of surveillance systems in roads, stations, malls, traffic or airports has led to a huge amount of data that needs to be analyzed for safety, retrieval or even commercial reasons. The task of automatically detecting frames with anomalous events from long duration video sequences has concerned

Gait identification has recently gained attention as a method of identifying individuals at a distance. It utilizing gait as identification for the certain distinct phases and stances. Human gait analysis is applicable for different areas like surveillance, medical diagnosis, car parking, banks, and video communication etc. It can be detected in low resolution video and it recognizable from distance. Gait identification is a term used in computer vision community to refer automatic extraction of visuals cues that characterize the motion of a walking person in video for identification purposes. For the gait image, gait feature extraction is extracted by horizontal alignments.

In case of road anomalies, bump testing samples were correctly recognized with accuracy 92.3\%. For driving behaviors, normal driving behaviors testing samples were correctly classified with accuracy 95.3\% while the abnormal behaviors classified with accuracy 98\%. In this experiment, k-NN achieves total recognition rate 95.9\% in determining the type of driving event. k-NN results are shown in Figure \ref{fig:algorithms}.