## 3 Related Works  
Surveillance systems and pedestrian movement detection have been the subject of several articles in the related literature. The survey by [PERSON] and [PERSON] [15] highlighted the growing importance of intelligent surveillance systems with distributed architecture. Several important issues were identified, including: detection and recognition of moving objects; tracking and detection of anomalous movement patterns in video surveillance, behavior analysis; and recovering/storing images. Existent systems were classified in three categories, according to their _generation_: (i) a _first generation_ of analog Closed Circuit TeleVision (CCTV) systems that performed fairly good but were not easy to distribute and store; (ii) a _second generation_ of systems that combined computer vision technologies with CCTV for automation, which allowed increasing the surveillance efficiency by providing accurate event detection; and (iii) a _third generation_ of automatic systems that cover large areas by a distributed deploy, combining sensors, robust tracking algorithms, and optimized algorithms for managing large volumes of information. Taking into account the classification by [PERSON] and [PERSON], the system proposed in our research is within the third generation, as complex pattern detection methods are included and parallel computing techniques are applied in order to deal with multiple distributed sources of data. [PERSON] [16] presented a review of background subtraction methods, describing the main features of seven algorithms and analyzing their performance (processing speed, memory utilization and accuracy). The studied methods included: Running Gaussian Average (RGA), Temporal median filter, Mixture of Gaussians (MOG), Kernel Density Estimation (KDE), Sequential Kernel Density approximation, and Concurrence of image variations. A performance analysis was reported, based on the complexity of applying the methods over each pixel (for the case of processing speed and memory utilization) and on categorizing into limited, intermediate or high (L, M, H) accuracy provided. Analysis showed that Running Gaussian Average obtained the best processing speed and the lower memory utilization, while Mixture of Gaussians and Kernel Density Estimation were the best methods regarding accuracy. Table 1 summarizes the main features (time and space complexity, and accuracy) of the methods studied by [PERSON]. [PERSON] [17] presented a system capable of detecting apparent movement on images (caused by camera movements). Two types of methods were reviewed: those that detect movement from the difference of consecutive images and those that detect the movement from a single image (e.g., optical flow methods, occasionally used for background subtraction). The author concluded that methods for detecting from a single image have a higher computational cost and introduce noise to the process. Thus, global alignment methods using points with correspondence were used. The system proposed by [PERSON] obtains an aligned image without apparent movement and both original and aligned images are sent to a segmentation module composed of three layers that apply background subtraction, labeling, and process grouping. The output of the segmentation module results in a set of regions of interest (_blobs_). Blobs are sent to the tracking module that applies filters, including the algorithm by [PERSON] [18] and also Kalman filters [19] to decide if apparent movement is detected or not. Results close to 90% were achieved without using tracking and almost 100% using tracking. Regarding pedestrian detection/tracking, [PERSON] [20] presented a system for counting people based on detecting the moment in which a person crosses a previously established virtual line. The author proposed counting people on images captured with a camera placed on the roof, in order to eliminate occlusion problems.