Digital Image Processing

P. Karpagavalli ¹, V. G. Janani ¹, A. V. Ramprasad ¹

Affiliations
1 K.L.N. College of Engineering., Sivagangai, IN

Abstract

In this paper, we consider a human activity recognition approach that only requires single video example per activity. We introduce silhouette segmentation for extracting the foreground objects from background video. We used to represent a single human activity video by using bounding box representations, from that we can calculate height, width, aspect ratio, blob area, normalized bounding box specifying the sub-event’s spatial location, averaging the coordinates of all bounding boxes and Centroid, where human motion and body configuration is observed and tracked. We present an approach for measuring similarity between visual entities (images or videos) based on matching internal self-similarities .This intrinsic information is used together with statistical features and shape based approaches to recognize and classify Human activities. Spatio temporal features can be used for feature extraction of particular activity .Further; we had used K-NN, RVM, SVM classifiers in order to recognize the abnormal activity form human activity by using active learning algorithm and also measure the performance between them.

Keywords

Activity Recognition, Active Learning Algorithm, Ada Boost Classifier, K-NN Classifier, RVM Classifier, Silhouette Segmentation, SVM Classifier, Spatio-Temporal Features.

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P. Karpagavalli ¹, T. Saranyaa ², A. V. Ramprasad ¹

Affiliations
1 K.L.N. College of Engineering, Sivagangai, IN
2 K.L.N College of Engineering, Sivagangai, IN

Abstract

Pedestrian counting plays an important role in public safety and intelligent transportation systems. The techniques of tracking a single pedestrian becomes impractical and complicated, when the scenes are densely crowded. In this paper, first step is extracting foreground objects from the background by applying ROI mask and then the pedestrians are detected by segmentation method. Second, the large sets of features extracted are reduced by using SLFs in order to attain high accuracy for counting number of pedestrians. The bounding box representation is used for counting number of pedestrians in a crowded environment. The number of people can be estimated by using connected component labeling method. Finally, by using above features reliable people counting in crowd environment can be achieved. The proposed method is robust for illumination changes and working well in public places for safety of the people, such as railway station, hospitals, shopping malls, etc. For further implementation, consider the proposed approach on a dataset consisting of a large number of people and study how to refine the computational efficiency for large scale datasets.

Keywords

Pedestrian Counts, Region of Interest (ROI), Statistical Landscape Features (SLFs).

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