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Human actions are defined as the coordinated movement of different body parts in a meaningful way to describe different aspects of human behavior. Recognizing human actions through computer vision is a trending research area as it has applications in both indoor and outdoor environments. Human action recognition (HAR) has a broad application area in the field of surveillance, patient’s behaviour detection, video retrieval, sports video analysis, human-computer interaction, etc. However, processing of the action videos is a challenging and complex task. This motivates to develop a good HAR algorithm with better video representation, feature extraction and classification capabilities to recognize different action classes effectively. In this regard, at first a semisupervised tree and 3D local feature based HAR paradigm (sST-3DF) is developed. Here, a motion history image (MHI) based interest point refinement is proposed to remove the noisy interest points. Histogram of oriented gradient (HOG) and histogram of optical flow (HOF) techniques are extended from spatial to spatio-temporal domain to preserve the temporal information. These local features are used to build the trees for the random forest technique. During tree building, a semi-supervised learning is proposed for better splitting of data points at each node. For recognition of an action from the video, mutual information is estimated for all the extracted interest points to each of the trained class by passing them through the random forest. Next, a two-stream sequential network is developed to leverage sequential and shape information for recognition of human actions more efficiently. In this technique, a deep bi-directional long short term memory (DBiLSTM) network is constructed to model temporal relationship between action frames through sequential learning. Action information in each frame is extracted using pre-trained convolutional neural network (CNN). During the shape learning, the knowledge of shape i

Tracking and study of behavioural changes of human beings through vision is a challenging task. For surveillance, automated systems are important which can observe the traffic and can detect the abnormality. For tracking human or any kind of object, colour feature based mean shift technique is widely used. This technique uses Bhattacharya coefficient to locate the object based on the maximisation of the similarity function between object model and candidate model. Traditional mean shift algorithm fails when the object having large motion, occlusion, corrupted frames etc. In addition to that, the technique is not automatic to initiate the tracking. To overcome all these problems, this thesis work proposed a technique which uses three additional modules to the traditional method to make it more efficient. The proposed modules used human detection by modelling through star skeletonization, followed by block search algorithm and occlusion handling. Block search algorithm helped to supply an overlapping area to candidate model to continue the track when tracking fails due to fast motion. Occlusion handling helped in initiating the tracking after prolonged period of occlusion. The proposed method has been tested on real time data and it outperforms the conventional method effectively to overcome the mentioned problems up to large extent. Human activity recognition is a hierarchical procedure which confirms abnormality step by step. Low level activity recognition is a trajectory based application in which trajectory of tracks of a human being helps to detect the abnormal events like person fell down, illegal entry, abnormal loitering, line formation etc. At high level, human pose will be detected by the help of shape based human pose detection. The main aim of the system is to make a person independent real-time human activity recognition with decreased false alarm rates.

Human actions are defined as the coordinated movement of different body parts in a meaningful way to describe different aspects of human behavior. Recognizing human actions through computer vision is a trending research area as it has applications in both indoor and outdoor environments. Human action recognition (HAR) has a broad application area in the field of surveillance, patient’s behaviour detection, video retrieval, sports video analysis, human-computer interaction, etc. However, processing of the action videos is a challenging and complex task. This motivates to develop a good HAR algorithm with better video representation, feature extraction and classification capabilities to recognize different action classes effectively. In this regard, at first a semisupervised tree and 3D local feature based HAR paradigm (sST-3DF) is developed. Here, a motion history image (MHI) based interest point refinement is proposed to remove the noisy interest points. Histogram of oriented gradient (HOG) and histogram of optical flow (HOF) techniques are extended from spatial to spatio-temporal domain to preserve the temporal information. These local features are used to build the trees for the random forest technique. During tree building, a semi-supervised learning is proposed for better splitting of data points at each node. For recognition of an action from the video, mutual information is estimated for all the extracted interest points to each of the trained class by passing them through the random forest. Next, a two-stream sequential network is developed to leverage sequential and shape information for recognition of human actions more efficiently. In this technique, a deep bi-directional long short term memory (DBiLSTM) network is constructed to model temporal relationship between action frames through sequential learning. Action information in each frame is extracted using pre-trained convolutional neural network (CNN). During the shape learning, the knowledge of shape i

Tracking and study of behavioural changes of human beings through vision is a challenging task. For surveillance, automated systems are important which can observe the traffic and can detect the abnormality. For tracking human or any kind of object, colour feature based mean shift technique is widely used. This technique uses Bhattacharya coefficient to locate the object based on the maximisation of the similarity function between object model and candidate model. Traditional mean shift algorithm fails when the object having large motion, occlusion, corrupted frames etc. In addition to that, the technique is not automatic to initiate the tracking. To overcome all these problems, this thesis work proposed a technique which uses three additional modules to the traditional method to make it more efficient. The proposed modules used human detection by modelling through star skeletonization, followed by block search algorithm and occlusion handling. Block search algorithm helped to supply an overlapping area to candidate model to continue the track when tracking fails due to fast motion. Occlusion handling helped in initiating the tracking after prolonged period of occlusion. The proposed method has been tested on real time data and it outperforms the conventional method effectively to overcome the mentioned problems up to large extent. Human activity recognition is a hierarchical procedure which confirms abnormality step by step. Low level activity recognition is a trajectory based application in which trajectory of tracks of a human being helps to detect the abnormal events like person fell down, illegal entry, abnormal loitering, line formation etc. At high level, human pose will be detected by the help of shape based human pose detection. The main aim of the system is to make a person independent real-time human activity recognition with decreased false alarm rates.

Human actions are defined as the coordinated movement of different body parts in a meaningful way to describe different aspects of human behavior. Recognizing human actions through computer vision is a trending research area as it has applications in both indoor and outdoor environments. Human action recognition (HAR) has a broad application area in the field of surveillance, patient’s behaviour detection, video retrieval, sports video analysis, human-computer interaction, etc. However, processing of the action videos is a challenging and complex task. This motivates to develop a good HAR algorithm with better video representation, feature extraction and classification capabilities to recognize different action classes effectively. In this regard, at first a semisupervised tree and 3D local feature based HAR paradigm (sST-3DF) is developed. Here, a motion history image (MHI) based interest point refinement is proposed to remove the noisy interest points. Histogram of oriented gradient (HOG) and histogram of optical flow (HOF) techniques are extended from spatial to spatio-temporal domain to preserve the temporal information. These local features are used to build the trees for the random forest technique. During tree building, a semi-supervised learning is proposed for better splitting of data points at each node. For recognition of an action from the video, mutual information is estimated for all the extracted interest points to each of the trained class by passing them through the random forest. Next, a two-stream sequential network is developed to leverage sequential and shape information for recognition of human actions more efficiently. In this technique, a deep bi-directional long short term memory (DBiLSTM) network is constructed to model temporal relationship between action frames through sequential learning. Action information in each frame is extracted using pre-trained convolutional neural network (CNN). During the shape learning, the knowledge of shape i

Tracking and study of behavioural changes of human beings through vision is a challenging task. For surveillance, automated systems are important which can observe the traffic and can detect the abnormality. For tracking human or any kind of object, colour feature based mean shift technique is widely used. This technique uses Bhattacharya coefficient to locate the object based on the maximisation of the similarity function between object model and candidate model. Traditional mean shift algorithm fails when the object having large motion, occlusion, corrupted frames etc. In addition to that, the technique is not automatic to initiate the tracking. To overcome all these problems, this thesis work proposed a technique which uses three additional modules to the traditional method to make it more efficient. The proposed modules used human detection by modelling through star skeletonization, followed by block search algorithm and occlusion handling. Block search algorithm helped to supply an overlapping area to candidate model to continue the track when tracking fails due to fast motion. Occlusion handling helped in initiating the tracking after prolonged period of occlusion. The proposed method has been tested on real time data and it outperforms the conventional method effectively to overcome the mentioned problems up to large extent. Human activity recognition is a hierarchical procedure which confirms abnormality step by step. Low level activity recognition is a trajectory based application in which trajectory of tracks of a human being helps to detect the abnormal events like person fell down, illegal entry, abnormal loitering, line formation etc. At high level, human pose will be detected by the help of shape based human pose detection. The main aim of the system is to make a person independent real-time human activity recognition with decreased false alarm rates.

Tracking and study of behavioural changes of human beings through vision is a challenging task. For surveillance, automated systems are important which can observe the traffic and can detect the abnormality. For tracking human or any kind of object, colour feature based mean shift technique is widely used. This technique uses Bhattacharya coefficient to locate the object based on the maximisation of the similarity function between object model and candidate model. Traditional mean shift algorithm fails when the object having large motion, occlusion, corrupted frames etc. In addition to that, the technique is not automatic to initiate the tracking. To overcome all these problems, this thesis work proposed a technique which uses three additional modules to the traditional method to make it more efficient. The proposed modules used human detection by modelling through star skeletonization, followed by block search algorithm and occlusion handling. Block search algorithm helped to supply an overlapping area to candidate model to continue the track when tracking fails due to fast motion. Occlusion handling helped in initiating the tracking after prolonged period of occlusion. The proposed method has been tested on real time data and it outperforms the conventional method effectively to overcome the mentioned problems up to large extent. Human activity recognition is a hierarchical procedure which confirms abnormality step by step. Low level activity recognition is a trajectory based application in which trajectory of tracks of a human being helps to detect the abnormal events like person fell down, illegal entry, abnormal loitering, line formation etc. At high level, human pose will be detected by the help of shape based human pose detection. The main aim of the system is to make a person independent real-time human activity recognition with decreased false alarm rates.

Human actions are defined as the coordinated movement of different body parts in a meaningful way to describe different aspects of human behavior. Recognizing human actions through computer vision is a trending research area as it has applications in both indoor and outdoor environments. Human action recognition (HAR) has a broad application area in the field of surveillance, patient’s behaviour detection, video retrieval, sports video analysis, human-computer interaction, etc. However, processing of the action videos is a challenging and complex task. This motivates to develop a good HAR algorithm with better video representation, feature extraction and classification capabilities to recognize different action classes effectively. In this regard, at first a semisupervised tree and 3D local feature based HAR paradigm (sST-3DF) is developed. Here, a motion history image (MHI) based interest point refinement is proposed to remove the noisy interest points. Histogram of oriented gradient (HOG) and histogram of optical flow (HOF) techniques are extended from spatial to spatio-temporal domain to preserve the temporal information. These local features are used to build the trees for the random forest technique. During tree building, a semi-supervised learning is proposed for better splitting of data points at each node. For recognition of an action from the video, mutual information is estimated for all the extracted interest points to each of the trained class by passing them through the random forest. Next, a two-stream sequential network is developed to leverage sequential and shape information for recognition of human actions more efficiently. In this technique, a deep bi-directional long short term memory (DBiLSTM) network is constructed to model temporal relationship between action frames through sequential learning. Action information in each frame is extracted using pre-trained convolutional neural network (CNN). During the shape learning, the knowledge of shape i