Systems and methods for detecting anomalies in videos
AssigneeThe Florida International University Board of Trustees
InventorsMohammadhadi Amini, Khandaker Mamun Ahmed, Shamim Yazdani
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Abstract
Systems and methods are provided for detecting anomalies in videos (e.g., surveillance videos) in a distributed manner to enhance data privacy and improve model robustness, model accuracy, and scalability of the system. Computer-based systems and methods can ensure enhanced privacy among different computing devices/clients while training a machine learning model without the requirement of data sharing with a server. A data trimming method, a histogram-based noise cleansing method, a frame selection method, a data distribution method among different clients, and a convolutional neural network and recurrent neural network model can be employed for video classification.
Claims20
A system for anomaly detection in a video, the system comprising:
- a processor;
- and
- a machine-readable medium in operable communication with the processor and having instructions stored thereon that, when executed by the processor, perform the following steps:
- receiving video data of the video;
- selecting a threshold with respect to a number of frames of the video;
- distributing the video data to a plurality of edge device clients, such that each edge device clients receives a different portion of the video data based on the selected threshold;
- training, by the plurality of edge device clients, of a model that comprises a convolutional neural network (CNN) and a recurrent neural network (RNN), such that the training includes limited communication between a central server and the plurality of edge device clients;
- and
- after the training of the model, running the model on the video data to identify any anomalies in the video.
A method for anomaly detection in a video, the method comprising:
- receiving video data of the video;
- selecting a threshold with respect to a number of frames of the video;
- distributing the video data to a plurality of edge device clients, such that each edge device clients receives a different portion of the video data based on the selected threshold;
- training, by the plurality of edge device clients, of a model that comprises a convolutional neural network (CNN) and a recurrent neural network (RNN), such that the training includes limited communication between a central server and the plurality of edge device clients;
- and
- after the training of the model, running the model on the video data to identify any anomalies in the video.
A system for anomaly detection in a video, the system comprising:
- a processor;
- and
- a machine-readable medium in operable communication with the processor and having instructions stored thereon that, when executed by the processor, perform the following steps:
- receiving video data of the video;
- trimming the video data;
- noise cleansing the video data using histogram analysis;
- selecting a threshold with respect to a number of frames of the video;
- distributing the video data to a plurality of edge device clients, such that each edge device clients receives a different portion of the video data based on the selected threshold;
- training, by the plurality of edge device clients, of a model that comprises a convolutional neural network (CNN) and a recurrent neural network (RNN), such that the training includes limited communication between a central server and the plurality of edge device clients;
- and
- after the training of the model, running the model on the video data to identify any anomalies in the video,
- the RNN comprising two gated recurrent units (GRUs) and at least one dense layer after the two GRUs,
- the CNN comprising a plurality of sections and a global max pooling layer, each section of the plurality of sections comprising at least one convolutional layer, a batch normalization layer, and a max pooling layer,
- the step of running the model on the video data to identify any anomalies in the video comprising running the video data through the CNN and then subsequently through the RNN,
- the video being a surveillance video, and
- the anomalies in the video comprising incidents related to crimes being committed.
Dependencies are inferred from the claim text.