Evaluation of Moving Object Detection Based on Various Input Noise Using Fixed Camera

Abstract

ABSTRACT:Detecting and tracking objects in video has been as a research area of interest in the field of image processing and computer vision. This paper evaluates the performance of a novel method for object detection algorithm in video sequences. This process helps us to know the advantage of this method which is being used. The proposed framework compares the correct and wrong detection percentage of this algorithm. This method was evaluated with the collected data in the field of urban transport which include car and pedestrian in fixed camera situation. The results show that the accuracy of the algorithm will decreases because of image resolution reduction.

Using GLCM features in Haar wavelet transformed space for moving object classification

“…It illustrates that by increasing the number of features, which extracted from different sub-bands, the accuracy of the classification will increase. Since the sensitivity of surveillance cameras in different colour spectrums are different, we have examined the results of extracted features from each frame by applying a wavelet in red, green, and blue separately, as shown in Table 3 [22,23]. Fig.…”