Vehicle Detection and Tracking Techniques: A Concise Review

Vehicle detection and tracking applications play an important role for civilian and military applications such as in highway traffic surveillance control, management and urban traffic planning. Vehicle detection process on road are used for vehicle tracking, counts, average speed of each individual vehicle, traffic analysis and vehicle categorizing objectives and may be implemented under different environments changes. In this review, we present a concise overview of image processing methods and analysis tools which used in building these previous mentioned applications that involved developing traffic surveillance systems. More precisely and in contrast with other reviews, we classified the processing methods under three categories for more clarification to explain the traffic systems

Automatic human ear detection approach using modified adaptive search window technique

The human ear biometric recognition plays an important role in the forensics specialty and has significant impact for biometrician scientists and researchers. Actually, many ear recognition researches showed promised results, but some issues such as manual detection process, efficiency and robustness aren’t attained a certain level of maturity. Therefore, the enhancement developing approaches still continuous to achieve limited successes. We propose an efficient, reliable and simple automatic human ear detection approach. This approach implement two stages: preprocessing and ear landmarks detection. We utilized the image contrast, Laplace filter and Gaussian blurring techniques to made enhancement on all images (increasing the contrast, reduce the noisy and smoothing processes). After that, we highlighted the ear edges by using the Sobel edge detector and determining the only white pixels of ear edges by applying the image substation method. The improvement focused on using the modified adaptive search window (ASW) to detect the ear region. Furthermore, our approach is tested on Indian Institute of Technology (IIT) Delhi standard ear biometric public dataset. Experimental results presented a well average detection rate 96% for 493 image samples from 125 persons and computational time almost ≈ 0.485 seconds which is evaluated with other previous works

Novel Shape Description for CBIR in Medical Application

Abstract. Image Retrieval for Medical Applications (IRMA) has received a significant research interest over the past decade as a promising approach to address the data management challenges posed by the rapidly increasing volume of medical image data collections in use and also to aid clinical medicine, research, and education relying on visual content in the data. The research presented in this paper was aimed to improve the retrieval performance of an images retrieval system in medical applications based on shape features. In general, the work consists of two phases: (1) enrollment phase, which consist of feature extraction based on developed method to extract the shape features, (2) retrieving phase, which use the Euclidian distance measure. The conducted tests were carried on 350 medical images from four types (i.e., abdominal CT scan, MRI, ultrasonic, X-ray) and give good precision and recall rates (94,89)

An Online Content Based Email Attachments Retrieval System

E-mail is one of the most popular programs used by most people today. As a result of the continuous daily use, thousands of messages are accumulated in the electronic box of most individuals, which make it difficult for them after a period of time to retrieve the attachments of these messages. Most Email providers constantly improved their search technology, but till now there is something could not be done; i.e., searching inside attachments. Some email providers like Gmail has added searching words inside attachments for some file types (.pdf files, .doc documents, .ppt presentations) but for image files this feature not supported till now. However, E-mail providers and even modern researchers have not focused on retrieving the image attachments in the E- mail box. The paper was aimed to introduce a novel idea of using Content based Image Retrieval (CBIR) in E-mail application to retrieve images from email attachments based on entire contents. The work main phases are: feature extraction based on color features and connect to Email server to read Emails, the second phase is retrieving similar image attachments. The tests carried on email inbox contain 100 messages with 500 image attachments and gave good precision and recall rates When the threshold value is less than or equal to 0.4

A Novel Method to Handle the Partial Occlusion and Shadow Detection of Highway Vehicles

This study proposes a novel method for resolving the partial occlusion and shadow of moving vehicles seen in a sequence of highway traffic images captured from a single roadside fixed camera position. The proposed method detects the shadow regions of foreground/moving vehicles in any direction with no human intervention. Also, it handles the partially occluded vehicles in three different states on the highway traffic scene. The moving vehicles are detected using the background subtraction method followed by using the dilation and erosion operations. In this step, every segmented moving vehicle has an extracted Feature Vector (FV) which is used to initialize the Shadow Searching Window (SSW). Then, a chromatic based analysis technique uses the semi mean value of RGB colour space of moving vehicle region pixels as a threshold value to discriminate the shadow of moving vehicles despite the direction of movement. Finally, the partially occluded vehicles are handled using a new training procedure based on previous estimations to calculate the new moving vehicle sizes. These sizes are employed to separate and highlight the partially occluded vehicles by drawing the bounding boxes for each moving vehicle

Survey on gap filling algorithms in Landsat 7 ETM+ images

In remote sensing images the gapping is a known phenomenon. There are several reasons for image gaps, e.g. shadowed area for SAR data sets, cloud coverage for optical imagery and instrument errors such as SLC-off failure. On May 13, 2003 the Scan Line Corrector (SLC) of Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor failed permanently causing around 20% of pixels per scene not scanned which become an obstacle and limitation for scientific applications of Landsat ETM+ data. Therefore, reconstruction of gap regions is an important issue in remote sensing image processing. This paper presents an inclusive review of methodologies that have been used to recover the gaps in Landsat7 ETM SLC-off images and the studies have been performed in this area. Then, the paper presents the derived conclusions and the directional to more efficiently researchs on Landsat7 SLC-off reconstruction

Improved Automatic Registration Adjustment of Multi-source Remote Sensing Datasets

Registration techniques are still considered challenging tasks to remote sensing users, especially after enormous increase in the volume of remotely sensed data being acquired by an ever-growing number of earth observation sensors. This surge in use mandates the development of accurate and robust registration procedures that can handle these data with varying geometric and radiometric properties. This paper aims to develop the traditional registration scenarios to reduce discrepancies between registered datasets in two dimensions (2D) space for remote sensing images. This is achieved by designing a computer program written in Visual Basic language following two main stages: The first stage is a traditional registration process by defining a set of control point pairs using manual selection, then comput the parameters of global affine transformation model to match them and resample the images. The second stage included matching process refinement by determining the shift value in control points (CPs) location depending on radiometric similarity measure. Then shift map technique was adjusted to adjust the process using 2nd order polynomial transformation function. This function has chosen after conducting statistical analyses, comparing between the common transformation functions (similarity, affine, projection and 2nd order polynomial). The results showed that the developed approach reduced the root mean square error (RMSE) of registration process and decreasing the discrepancies between registered datasets with 60%, 57% and 48% respectively for each one of the three tested datasets