Evaluation of Background Subtraction Algorithms with Post-processing

Processing a video stream to segment foreground objects from the background is a critical first step in many computer vision applications. Background subtraction (BGS) is a commonly used technique for achieving this segmentation. The popularity of BGS largely comes from its computational efficiency, which allows applications such as humancomputer interaction, video surveillance, and traffic monitoring to meet their real-time goals. Numerous BGS algorithms and a number of postprocessing techniques that aim to improve the results of these algorithms have been proposed. In this paper, we evaluate several popular, state-of-the-art BGS algorithms and examine how post-processing techniques affect their performance. Our experimental results demonstrate that post-processing techniques can significantly improve the foreground segmentation masks produced by a BGS algorithm. We provide recommendations for achieving robust foreground segmentation based on the lessons learned performing this comparative study. 1

Dekomposisi Citra Gerakan Dalam Rekaman Cctv Menggunakan Transformasi Wavelet Diskrit

Pada makalah ini dipresentasikan hasil dekomposisi data citra gerakan dalam rekaman CCTV menggunakan transformasi wavelet diskrit. Citra gerakan pada rekaman CCTV diambil dengan menggunakan background substraction. Dekomposisi data citra ditujukan untuk mendapatkan jumlah data citra yang lebih sedikit tetapi tidak menghilangkan cirri atau karakter citra aslinya. Nilai data pada setiap pixel citra yang sebelumnya tersusun dua dimensi diubah menjadi deret nilai pixel satu dimensi. Penerapan transformasi wavelet diskrit dilakukan dengan teknik pemfilteran menggunakan impuls daubechies orde 4 (Db4) wavelet. Pemfilteran ini menghasilkan dekomposisi sebuah sinyal dengan pengurangan setengah data di setiap level dekomposisi. Dari pengujian yang dilakukan, pada dekomposisi level 1 pengurangan data sebesar 49,99% dengan Perubahan parameter rata-rata nilai pixel 1,19% dan Perubahan pola pixel 1,93%. Pada level 2, pengurangan jumlah data 24,99% rata-rata nilai pixel 1,62% dan Perubahan pola pixel 2,46%. Pada level 3 Perubahan jumlah data 12,48% dengan Perubahan rata-rata pixel 2,32% dan pola pixel 3,82%. Pada level 4 Perubahan jumlah data mencapai 6,22% dengan Perubahan rata-rata nilai pixel 2,31% dengan Perubahan pola 4,57% dari citra aslinya. Dari hasil tersebut dapat disimpulkan bahwa transformasi wavelet dapat digunakan untuk memperkecil jumlah data citra tanpa kehilangan cirri atau karakteristik aslinya

Scalable software architecture for on-line multi-camera video processing

In this paper we present a scalable software architecture for on-line multi-camera video processing, that guarantees a good trade off between computational power, scalability and flexibility. The software system is modular and its main blocks are the Processing Units (PUs), and the Central Unit. The Central Unit works as a supervisor of the running PUs and each PU manages the acquisition phase and the processing phase. Furthermore, an approach to easily parallelize the desired processing application has been presented. In this paper, as case study, we apply the proposed software architecture to a multi-camera system in order to efficiently manage multiple 2D object detection modules in a real-time scenario. System performance has been evaluated under different load conditions such as number of cameras and image sizes. The results show that the software architecture scales well with the number of camera and can easily works with different image formats respecting the real time constraints. Moreover, the parallelization approach can be used in order to speed up the processing tasks with a low level of overhea

Automated Computer-Based Enumeration of Acellular Capillaries for Assessment of Diabetic Retinopathy

Diabetic retinopathy (DR) is the most common complications of diabetes; if untreated the DR can lead to a vision loss. The treatment options for DR are limited and the development of newer therapies are of considerable interest. Drug screening for the retinopathy treatment is undertaken using animal models in which the quantification of acellular capillaries (capillary without any cells) is used as a marker to assess the severity of retinopathy and the treatment response. The traditional approach to quantitate acellular capillaries is through manual counting. The purpose of this investigation was to develop an automated technique for the quantitation of acellular capillaries using computer-based image processing algorithms. We developed a custom procedure using the Python, the medial axis transform (MAT) and the connected component algorithm. The program was tested on the retinas of wild-type and diabetic mice and the results were compared to single blind manual counts by two independent investigators. The program successfully identified and enumerated acellular capillaries. The acellular capillary counts were comparable to the traditional manual counting. In conclusion, we developed an automated computer-based program, which can be effectively used for future pharmacological development of treatments for DR. This algorithm will enhance consistency in retinopathy assessment and reduce the time for analysis, thus, contributing substantially towards the development of future pharmacological agents for the treatment of DR

Tracking Of Rigid Objects

This report mainly focuses on the basic theories and approaches used in background subtraction. An insight to background subtraction and its basic theory are discussed

Anomaly activity classification in the grocery stores

Nowadays, because of the growing number of robberies in shopping malls and grocery stores, automatic camera’s applications are vital necessities to detect anomalous actions. These events usually happen quickly and unexpectedly. Therefore, having a robust system which can classify anomalies in a real-time with minimum false alarms is required. Due to this needs, the main objective of this project is to classify anomalies which may happen in grocery stores. This objective is acquired by considering properties, such as; using one fixed camera in the store and the presence of at least one person in the camera view. The actions of human upper body are used to determine the anomalies. Articulated motion model is used as the basis of the anomalies classification design. In the design, the process starts with feature extraction and followed by target model establishment, tracking and action classification. The features such as color and image gradient built the template as the target model. Then, the models of different upper body parts are tracked during consecutive frames by the tracking method which is sum of square differences (SSD) combined with the Kalman filter as the predictor. The spatio-temporal information as the trajectory of limbs gained by tracking part is sent to proposed classification part. For classification, three different scenarios are studied: attacking cash machine, cashier’s attacking and making the store messy. In implementing these scenarios, some events were introduced. These events are; basic (static) events which are the static objects in the scene, spatial events which are those actions depend on coordinates of body parts and spatio-temporal events in which these actions are tracked in consecutive frames. At last, if one of the scenarios happens, an anomalous action will be detected. The results show the robustness of the proposed methods which have the minimum false positive error of 7% for the cash machine attack and minimum false negative error of 19% for the cashier’s attacking scenario