Anomaly detection for video-based surveillance using covariance features and modeling of sequences via LSTMS

In this thesis, we propose three different methods for anomaly detection in surveillance videos based on autoregressive modeling of observation likelihoods. By means of the methods we propose, normal (typical) events in a scene are learned in a probabilistic framework by estimating the features of consecutive frames taken from the surveillance camera. The proposed methods are based on long short-term memory (LSTM), linear regression, and support vector regression (SVR). To decide whether an observation sequence (i.e. a small video patch) contains an anomaly or not, its likelihood under the modeled typical observation distribution is thresholded. An anomaly is decided to be present if the threshold is exceeded. Due to its effectiveness in object detection and action recognition applications, covariance features are used in this study to compactly reduce the dimensionality of the shape and motion cues of spatiotemporal patches obtained from the video segments. Our proposed methods that are based on the final state vector of LSTM and support vector regression (SVR) applied to mean covariance features, and achieve an average performance of up to 0.95 area under curve (AUC) on benchmark datasets

Object-centric video anomaly detection with covariance features [Kovaryans öznitelikleri ile nesne merkezli video anomali sezimi]

In this paper, we propose four different methods for object-centric anomaly detection in surveillance videos based on autoregressive probability estimation. By means of the methods we propose, normal (typical) events in a scene are learned in a probabilistic framework by estimating the features of consecutive frames taken from the surveillance camera. To decide whether an observation sequence (i.e. a small video patch) contains an anomaly or not, its likelihood under the modeled typical observation distribution is thresholded. Due to its effectiveness in object detection and action recognition applications, covariance features are used in this study to compactly reduce the dimensionality of the shape and motion cues of spatiotemporal patches obtained from the video segments. By employing an object detection module to determine the important active regions in a scene with high detection rate, we propose new long-short term memory (LSTM), linear regression, and Gaussian mixture based methods to model the probability density of observation sequences. The most successful methods we propose achieves an average performance of 0.843 and 0.935 AUC scores respectively on two publicly available benchmark datasets

Video anomaly detection with autoregressive modeling of covariance features

In this paper, we propose three different methods for anomaly detection in surveillance videos based on modeling of observation likelihoods. By means of the methods we propose, normal (typical) events in a scene are learned in a probabilistic framework by estimating the features of consecutive frames taken from the surveillance camera. The proposed methods are based on long short-term memory (LSTM) and linear regression. To decide whether an observation sequence (i.e., a small video patch) contains an anomaly or not, its likelihood under the modeled typical observation distribution is thresholded. An anomaly is decided to be present if the threshold is exceeded. Due to its effectiveness in object detection and action recognition applications, covariance features are used in this study to compactly reduce the dimensionality of the shape and motion cues of spatiotemporal patches obtained from the video segments. The two most successful methods are based on the final state vector of LSTM and support vector regression applied to mean covariance features and achieve an average performance of up to 0.95 area under curve on benchmark datasets

Intraoperative cytological diagnosis of brain tumours: A preliminary study using a deep learning model

© 2022 John Wiley & Sons Ltd.Background: Intraoperative pathological diagnosis of central nervous system (CNS) tumours is essential to planning patient management in neuro-oncology. Frozen section slides and cytological preparations provide architectural and cellular information that is analysed by pathologists to reach an intraoperative diagnosis. Progress in the fields of artificial intelligence and machine learning means that AI systems have significant potential for the provision of highly accurate real-time diagnosis in cytopathology. Objective: To investigate the efficiency of machine-learning models in the intraoperative cytological diagnosis of CNS tumours. Materials and Methods: We trained a deep neural network to classify biopsy material for intraoperative tissue diagnosis of four major brain lesions. Overall, 205 medical images were obtained from squash smear slides of histologically correlated cases, with 18 high-grade and 11 low-grade gliomas, 17 metastatic carcinomas, and 9 non-neoplastic pathological brain tissue samples. The neural network model was trained and evaluated using 5-fold cross-validation. Results: The model achieved 95% and 97% diagnostic accuracy in the patch-level classification and patient-level classification tasks, respectively. Conclusions: We conclude that deep learning-based classification of cytological preparations may be a promising complementary method for the rapid and accurate intraoperative diagnosis of CNS tumours