# Pedestrian Localization in a Video Sequence Using Motion Detection and Active Shape Models
## 2 Related Work  
Background subtraction is one of the main methods to address the problem of motion detection [10]. It provides separation between the background and the objects of interest, but it is sensitive to dynamic changes due to lighting and occlusions. There are different techniques to perform motion detection by background subtraction (basic background modeling, statistical background modeling, among others [11]). [PERSON] et al. [12] proposed a method that looks for a dynamic background region to reduce false positives in the video obtained by a CCTV (closed-circuit television) camera. This method was evaluated with the CDnet 2012/2014 datasets (CDNET, [[http://jacarini.dinf.usherbrooke.ca](http://jacarini.dinf.usherbrooke.ca)]([http://jacarini.dinf.usherbrooke.ca](http://jacarini.dinf.usherbrooke.ca)), accessed on 8 January 2020). Average precisions of 86.50% and 76.68% were obtained on the CDnet2012 and CDnet2014 databases, respectively. [PERSON] and [PERSON] [13] used a combination of classifiers that improves background subtraction, reducing false detections. Although the image had variability in color, shadows, and lighting, an average precision of 70% was obtained in their work. [PERSON] and [PERSON] [14] presented a new technique that improves the frame difference method by classifying at the pixel level the first frames as background and their planes with a correlation coefficient. They compared their results with different original images. The best precision was 0.9984 in a camouflaged image. [PERSON] et al. [15] presented 12 techniques based on motion detection to find the object of interest. They used the CDnet2014 dataset, obtaining the best result with the GMM (Gaussian mixture model) technique with a specificity of 0.99593. About ASM, a wide variety of literature related to the adjustment of forms in organs and parts of the human body, such as hands or faces, is cited [16; 17; 18]. [PERSON] and [PERSON] [19] mention the construction of a point distribution model (PDM) where the silhouette of the human and its anatomical joints can be combined to build the ASM model, and subsequently be able to segment the modeled body as new images. The use of deformable methods in pedestrian detection is limited because ASM is considered to require great time consumption due to the computational load [20]. For this reason, most of the investigations are based on the detection of static people (standing pose and standing facing the camera). Although it should be mentioned how walking people are detected with ASM, this is done with gait recognition techniques [21]. [PERSON] and [PERSON] [22] made a detector that works for poses and views, even with two or three rigid pedestrians. Pedestrian detection is performed by means of an ellipsoidal silhouette trained with 40 characteristic points of the image (landmarks) using the Kalman filter. [PERSON] et al. [23] actively applied a model to detect pedestrians in a video sequence using a different number of landmarks (10, 14, 21, and 42) and received three lineups of silhouettes of a single person. The contour detection was performed, and the normalized error between the actual ground landmarks and the estimated landmarks was calculated: the mean error in the distance between such points was 2.74 pixels with 21 landmarks, and it was 2.42 pixels with 42 landmarks. [PERSON] and [PERSON] [24] proposed a model based on an exoskeleton to detect human poses. They used background subtraction and a pattern matching algorithm in the fitting process. The accuracy in three poses resulted in 97.32%, 93.3%, and 80.62%. [PERSON] et al. [25] identified the way pedestrians walk. Pedestrians were detected with ASM, using 32 landmarks for the silhouette of a human from images of 720 \(\times\) 480 pixels, reaching up to 90% accuracy. [PERSON] and [PERSON] [26] detected pedestrians using active models using infrared (IR) images and visible-light cameras that allow pedestrians to be followed in degraded and low-light environments.