Reliable real-time video background subtraction: mixture of Gaussians approach

Секция 9. Распознавание образов, информационные системы управленияBackground subtraction is a commonly used class of techniques for segmenting out objects of interest in the context of static camera for applications such as surveillance. Trivial background subtraction algorithms (e.g. frame differencing, median filter) can perform quite fast but they are not robust enough to be used in various computer vision problems. The main purpose of this paper is to present common background subtraction flow, describe Mixture of Gaussians model and discuss experimental results

CORRECTING FALSE SEGMENTATION IN VIDEO USING IMAGE OVER-SEGMENTATION

Moving objects detection is a fundamental step in many vision based applications. Background subtraction is the typical method. When scene exhibits pertinent dynamism method based on mixture of Gaussians is a good balance between accuracy and complexity, but fails due to two kinds of false segmentations i.e moving shadows incorrectly detected as objects and some actual moving objects not detected as moving objects. In computer vision, segmentation refers to process of partitioning a digital image in to multiple segments and goal of segmentation is to simplify and/or change representation of image in to something that is more meaningful and easier to analyse. A colour clustering based on k-means and image over-segmentation are used to segment the input frame into patches and shadow suppression done by HSV colour space, the outputs of mixture of Gaussians are combined with the colour clustered regions to a module for area confidence measurement. In this way, two major segment errors can be corrected. Experimental results show that the proposed approach can significantly enhance segmentation results

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

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/)

Video surveillance systems-current status and future trends

Within this survey an attempt is made to document the present status of video surveillance systems. The main components of a surveillance system are presented and studied thoroughly. Algorithms for image enhancement, object detection, object tracking, object recognition and item re-identification are presented. The most common modalities utilized by surveillance systems are discussed, putting emphasis on video, in terms of available resolutions and new imaging approaches, like High Dynamic Range video. The most important features and analytics are presented, along with the most common approaches for image / video quality enhancement. Distributed computational infrastructures are discussed (Cloud, Fog and Edge Computing), describing the advantages and disadvantages of each approach. The most important deep learning algorithms are presented, along with the smart analytics that they utilize. Augmented reality and the role it can play to a surveillance system is reported, just before discussing the challenges and the future trends of surveillance

Foreground detection of video through the integration of novel multiple detection algorithims

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityThe main outcomes of this research are the design of a foreground detection algorithm, which is more accurate and less time consuming than existing algorithms. By the term accuracy we mean an exact mask (which satisfies the respective ground truth value) of the foreground object(s). Motion detection being the prior component of foreground detection process can be achieved via pixel based and block based methods, both of which have their own merits and disadvantages. Pixel based methods are efficient in terms of accuracy but a time consuming process, so cannot be recommended for real time applications. On the other hand block based motion estimation has relatively less accuracy but consumes less time and is thus ideal for real-time applications. In the first proposed algorithm, block based motion estimation technique is opted for timely execution. To overcome the issue of accuracy another morphological based technique was adopted called opening-and-closing by reconstruction, which is a pixel based operation so produces higher accuracy and requires lesser time in execution. Morphological operation opening-and-closing by reconstruction finds the maxima and minima inside the foreground object(s). Thus this novel simultaneous process compensates for the lower accuracy of block based motion estimation. To verify the efficiency of this algorithm a complex video consisting of multiple colours, and fast and slow motions at various places was selected. Based on 11 different performance measures the proposed algorithm achieved an average accuracy of more than 24.73% than four of the well-established algorithms. Background subtraction, being the most cited algorithm for foreground detection, encounters the major problem of proper threshold value at run time. For effective value of the threshold at run time in background subtraction algorithm, the primary component of the foreground detection process, motion is used, in this next proposed algorithm. For the said purpose the smooth histogram peaks and valley of the motion were analyzed, which reflects the high and slow motion areas of the moving object(s) in the given frame and generates the threshold value at run time by exploiting the values of peaks and valley. This proposed algorithm was tested using four recommended video sequences including indoor and outdoor shoots, and were compared with five high ranked algorithms. Based on the values of standard performance measures, the proposed algorithm achieved an average of more than 12.30% higher accuracy results

AKURASI ALAT PENGHITUNG LALU LINTAS PLATO 2.1 BERBASIS PENGOLAHAN CITRA - BACKGROUND SUBSTRACTION (ACCURACY OF TRAFFIC COUNTER PLATO 2.1 BASED ON IMAGE PROCESSING - BACKGROUND SUBSTRACTION)

ABSTRAK Penempatan sensor fisik di dalam lapisan perkerasan jalan sudah tidak memungkinkan lagi untuk diterapkan mengingat banyaknya kendaraan berat yang melintas di ruas jalan dan kegiatan overlay yang menyebabkan sensor mudah tidak berfungsi. PLATO 2.1 merupakan teknologi pengolahan citra video yang dikembangkan di Pusat Litbang Jalan dan Jembatan menggunakan metode background substraction. Penelitian ini bermaksud untuk mengetahui akurasi PLATO 2.1 dalam penghitungan volume lalu lintas. Metode yang dilakukan adalah membandingkan data volume lalu lintas yang dihitung secara manual dengan data yang dihitung menggunakan PLATO 2.1. Selanjutnya algoritma dalam PLATO 2.1 dimodifikasi dan digunakan untuk menghitung volume lalu lintas. Data volume lalu lintas yang dihasilkan dibandingkan terhadap data volume lalu lintas manual. Hasil penelitian menunjukkan bahwa perbedaan penghitungan kendaraan secara manual dan PLATO 2.1 adalah 30% untuk lalu lintas normal dan 3% untuk lalu lintas sedang. Setelah dilakukan modifikasi pada algoritma, yaitu dengan memisahkan algoritma pendeteksian motor dengan mobil dan mengganti metode area counting dari dua menjadi tiga area, ternyata dapat menghasilkan penghitungan yang lebih baik. Perbedaan penghitungan kendaraan PLATO 2.1 dan modifikasi PLATO 2.1 adalah 3% untuk lalu lintas normal dan 5% untuk lalu lintas sedang. Kata kunci: volume lalu lintas, background substraction, modifikasi algoritma, alat penghitung volume lalu lintas, pengolahan citra video ABSTRACT Placement of physical sensors in the pavement layer is no longer possible to apply given the many heavy vehicles that cross the road and overlay activities that cause the sensor easily does not work. PLATO 2.1 is a video image processing technology developed at IRE using the background substraction method. This research intends to know the accuracy of PLATO 2.1 in calculating traffic volume. The method used is to compare the traffic volume data calculated manually with the data calculated using PLATO 2.1. The next algorithm in PLATO 2.1 is modified and used to calculate the volume of traffic. The resulting traffic volume data is then compared against the traffic volume data manually. The results showed that the difference in vehicle count manually and PLATO 2.1 is 30% for normal traffic and 3% for medium traffic. After modification of the algorithm, separating the motor detection algorithm by car and changing the counting area method from two to three, it can produce better calculation. The difference in the calculation of the PLATO 2.1 vehicle and the modification of PLATO 2.1 is 3% for normal traffic and 5% for medium traffic. Keywords: traffic volume, background substraction, algorithm modification, traffic counters, video image processin

ENERGY-EFFICIENT LIGHTWEIGHT ALGORITHMS FOR EMBEDDED SMART CAMERAS: DESIGN, IMPLEMENTATION AND PERFORMANCE ANALYSIS

An embedded smart camera is a stand-alone unit that not only captures images, but also includes a processor, memory and communication interface. Battery-powered, embedded smart cameras introduce many additional challenges since they have very limited resources, such as energy, processing power and memory. When camera sensors are added to an embedded system, the problem of limited resources becomes even more pronounced. Hence, computer vision algorithms running on these camera boards should be light-weight and efficient. This thesis is about designing and developing computer vision algorithms, which are aware and successfully overcome the limitations of embedded platforms (in terms of power consumption and memory usage). Particularly, we are interested in object detection and tracking methodologies and the impact of them on the performance and battery life of the CITRIC camera (embedded smart camera employed in this research). This thesis aims to prolong the life time of the Embedded Smart platform, without affecting the reliability of the system during surveillance tasks. Therefore, the reader is walked through the whole designing process, from the development and simulation, followed by the implementation and optimization, to the testing and performance analysis. The work presented in this thesis carries out not only software optimization, but also hardware-level operations during the stages of object detection and tracking. The performance of the algorithms introduced in this thesis are comparable to state-of-the-art object detection and tracking methods, such as Mixture of Gaussians, Eigen segmentation, color and coordinate tracking. Unlike the traditional methods, the newly-designed algorithms present notable reduction of the memory requirements, as well as the reduction of memory accesses per pixel. To accomplish the proposed goals, this work attempts to interconnect different levels of the embedded system architecture to make the platform more efficient in terms of energy and resource savings. Thus, the algorithms proposed are optimized at the API, middleware, and hardware levels to access the pixel information of the CMOS sensor directly. Only the required pixels are acquired in order to reduce the unnecessary communications overhead. Experimental results show that when exploiting the architecture capabilities of an embedded platform, 41.24% decrease in energy consumption, and 107.2% increase in battery-life can be accomplished. Compared to traditional object detection and tracking methods, the proposed work provides an additional 8 hours of continuous processing on 4 AA batteries, increasing the lifetime of the camera to 15.5 hours

Simultaneous Tracking and Shape Estimation of Extended Objects

This work is concerned with the simultaneous tracking and shape estimation of a mobile extended object based on noisy sensor measurements. Novel methods are developed for coping with the following two main challenges: i) The computational complexity due to the nonlinearity and high-dimensionality of the problem and ii) the lack of statistical knowledge about possible measurement sources on the extended object