Over the last few years, surveillance CCTV cameras have rapidly grown to monitor human activities. Suspicious activities like assault, gun violence, kidnapping need to be observed in public places like malls, public roads, colleges, etc. There is a need for such a surveillance system that automatically recognizes human behavior, such as violent and non-violent actions. Action recognition has become an active research topic for researchers within the computer vision field. However, the human behavior recognition community has mainly focused only on regular actions like walking, running, jogging, etc. Though, detecting behavior in anomaly subjects like assault violence, gun violence, or general aggressive behavior has been comparatively less research in these specific events due to a lack of datasets and algorithms. Thus, there is an increasing demand for datasets to develop abnormal behavior algorithms that can classify anomaly actions. In this paper, the novel dataset is proposed named Human Behavior Dataset 2021 (HBD21). There are four categories of videos available in this dataset: Assault violence, Gun violence, Sabotage violence, and Normal events. This proposed dataset contains a total of 456 videos. Each video has the same length of each category. This paper aims to make a robust surveillance system framework with the help of a deep transfer learning approach and proposed a novel hybrid model. In this view, the current research work is categorized into three phases. Firstly, the preprocessing technique is applied to enhance the brightness of videos, and for resizing then, frames are extracted from each video. Secondly, the transfer learning-based Xception model is used to extract relevant features from frames. The third phase is a classification of behaviors in which a modified LSTM technique is applied. The model is trained using LSTM on the HBD21 dataset. Moreover, using proposed methods on the HBD21 dataset, the accuracy is obtained 97.25% overall.

One of the important objectives of image processing is to interpret the content of image

Generally for improving contrast in digital images, HE is the method that commonly used but in result it gives unnatural artifacts like intensity saturation, over-enhancement and noise amplification. To overcome these problems there was a need to partition the image histogram, at first image histogram was partitioned into two parts and then different transformation functions were applied on each partition. After that image histogram was partitioned into many partitions and same process was applied with some additional features. DHE is the multi histogram method and CLAHE is the extension of AHE. These methods are compared to HE and found that both methods give better result than HE but DHE method also gives better result than CLAHE.

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This paper simply presents a fully automated breast cancer detection system as “Deep Multi-view Breast cancer Detection” based on deep transfer learning. The deep transfer learning model i.e., Visual Geometry Group 16 (VGG 16) is used in this approach for the correct classification of Breast thermal images into either normal or abnormal. This VGG 16 model is trained with the help of Static as well as Dynamic breast thermal images dataset consisting of multi-view, single view breast thermal images. These Multi-view breast thermal images are generated in this approach by concatenating the conventional left, frontal and right view breast thermal images taken from the Database for Mastology Research with Infrared image for the first time in order to generate a more informative and complete thermal temperature map of breast for enhancing the accuracy of the overall system. For the sake of genuine comparison, three other popular deep transfer learning models like Residual Network 50 (ResNet50V2), InceptionV3 network and Visual Geometry Group 19 (VGG 19) are also trained with the same augmented dataset consisting of multi-view as well as single view breast thermal images. The VGG 16 based Deep Multi-view Breast cancer Detect system delivers the best training, validation as well as testing accuracies as compared to their other deep transfer learning models. The VGG 16 achieves an encouraging testing accuracy of 99% on the Dynamic breast thermal images testing dataset utilizing the multi-view breast thermal images as input. Whereas the testing accuracies of 95%, 94% and 89% are achieved by the VGG 19, ResNet50V2, InceptionV3 models respectively over the Dynamic breast thermal images testing dataset utilizing the same multi-view breast thermal images as input.

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