AUTO GMM-SAMT: An Automatic Object Tracking System for Video Surveillance in Traffic Scenarios

Statistical feature bag based background subtraction for local change detection

“…In the second part of the experiment, validation of the proposed technique is carried out by three performance evaluation measures: precision, recall and F-measure as in [5,34,14]. In the third part a brief discussion about the proposed technique with its advantages and disadvantages is provided.…”

“…In an effort to address such an issue, we have used three ground-truth based indices: precision, recall and F-measure as in [5,14,34]. It may be noted that all these measures should be high for better detection accuracy.…”

Systematic Review and Classification on Video Surveillance Systems

“…Video detecting category addresses issues regarding to automatic detection of anomalous, forbidden, dangerous events or abandoned object (counting moving people, ship detection, after-the-fact event, intruder detection, trajectory-based unusual behavior detection, motion detection, mult iple moving object detection, face detection, pedestrian detection, vehicle detection, unattended object detection, etc) . Video encoding [91], [92], [93], [94], [95], [96], [97], [98], [99], [100], [101], [102], [103], [16], [104] [105], [106], [107], [108], [109], [110], [7], [111] [112], [113], [114], [115] [116],[8], [117] Object detection is performed by co mmon statistical learning techniques with dynamically learning background model of the scene and applies the reference model to find out which section of the scene match with mov ing object. Reasoning refers to generating new explanations, facts and knowledge of dynamic scenes by applying inference engine and method (rule and case based reasoning, Bayesian network, decision tree).…”

Background initialization and foreground segmentation for bootstrapping video sequences

“…The MoG-based methods are effective for dynamic background scenes with multiple background variations, but they are sensitive to noise and illumination changes. Several existing MoG-based approaches are proposed to improve their performances by adaptation of some MoG parameters [5], such as the number of components [6,7], weights, mean, and variance [8][9][10][11], learning rate [8,9,12,13], and feature type [9,[14][15][16][17], and by smoothing among spatially and temporally neighboring pixels using spatial and temporal dependencies [18]. In general, a training duration without foreground objects (non-bootstrapping) is required and some ghost (false positive) objects may be detected when some foreground objects change their motion status (static or moving) suddenly.…”