Moving object recognition and detection is very crucial for video surveillance. In this paper, we... more Moving object recognition and detection is very crucial for video surveillance. In this paper, we present a comparative analysis between the various motion detection algorithms like background subtraction, Kalman filter, Mean Shift and Optical flow. The accumulative optical flow method is employed in order to obtain and retain a stable background image and cope with changes in environmental conditions. The performance of optical flow in terms of tracking and detection is much improved and accurate as compared to rest of the other algorithms.From the comparison it has been noted that the optical flow algorithm have outperformed the kalman and the mean shift algorithm .
This paper focuses on the hand gesture recognition using the various feature extraction technique... more This paper focuses on the hand gesture recognition using the various feature extraction techniques and SVM as a classifier. Her we have proposed the hybrid approach using SIFT and HoG combined as a feature extraction technique and gestures classification done using SVM linear kernel function.The accumulative multi class SVM method is employed in order to obtain a classification of the multiple gestures. In this computer age the hand gesture recognition is one of the important domain of the computer application wherein the human computer interaction is done without any contact. Various research are ongoing in order to produce the cost effective and robust system design in this field. We have also proposed our model with max 97% accuracy with 10 set of gesture. Keyword: SVM, HoG, SIFT, Hand Gesture recognition, Gesture, HC
A comparative analysis of color image segmentation of three different techniques is presented in ... more A comparative analysis of color image segmentation of three different techniques is presented in this paper with different noise levels. The robustness of each algorithm is checked on the basis of its accuracy of the clusters mapped. We have taken K-Means, FCM and Density based image segmentation approach for the analysis. In these methods of segmentation, the objects are distinguished clearly from the background. Basically Image is that type of information which has to be processed effectively and correctly. Segmentation of an image requires the separation or division of the image into regions of similar and multiple attribute. Image segmentation assigns a tag to each and every pixel in an image such that each pixel with the same tag share certain visual characteristic. Basic attribute for segmentation of an image is its luminance amplitude for an image and color components for a color image and its intensity level. Clustering is one of the methods which are used for segmentation. ...
Face Emotions Recognition using Compounded Local Binary Pattern (CLBP) Feature Descriptor with Support Vector Machine (SVM)
An automatic face feature detection problem from a static front pose image is discussed in this p... more An automatic face feature detection problem from a static front pose image is discussed in this paper. It deals with the classification and recognition of face emotions or expressions and also about the different signs of moods of different people. Support Vector machine (SVM) has been used to classify and identify the expressions of a given face into five categories. The five categories are neutral, sad, happy, disgust and angry. In this paper, we propose a reliable and robust facial feature vector exploring the (CLBP) Compound Local Binary Pattern for recognition of face emotion which overcomes many of the shortcomings of LBP(Local Binary Pattern) To implement the new improved feature descriptor the original LBP is joined with the add-on P bits in our proposed method that extracts both the magnitude and the sign of the differences between the centre and neighboring pixel values. JAFFE facial expression database is taken into consideration to perform the experiments.100% accuracy h...
Object Tracking is becoming very popular these days in the computer vision field. It is the proce... more Object Tracking is becoming very popular these days in the computer vision field. It is the process of tracking an object across a sequence of frames. Deep Sort is a very fast and powerful tracking algorithm. It has a practical way of approaching multiple object tracking problems. It uses the appearance information to track objects through occlusions and thereby reducing the identity switches. Performance evaluation and comparison have been performed on pedestrian tracking using the Deep Sort algorithm in conjunction with the various state-of-the-art object detectors: YOLO, SSD and FasterRCNN. Criteria for Evaluation, datasets used for evaluation, along with the quantitative results have been described and discussed in this work.
The growth in wireless adhoc network has inspired academicians, researchers and scientist’s commu... more The growth in wireless adhoc network has inspired academicians, researchers and scientist’s community to make MANET more reliable and promising network. Considering the aspect, we have developed a queueing model for manet which mitigate the packet drop and enhance QoS of the network. As packet dropping is one of the major security issue in Mobile Ad hoc Network. Therefore, this paper we mitigate the blocking packet dropping probability which resulted good performance of the MANET.
The Main motive of this paper is to put forward a comprehensive research review in the domain of ... more The Main motive of this paper is to put forward a comprehensive research review in the domain of Motion tracking algorithm in real time. the main focus is on the tracking algorithms and its application. More than 50 research paper on the motion tracking domain are studied and research works are acknowledged with proper references in order to exemplify the important issues and their relations to the various methods.
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Papers by Farah Ansari