Annotated videos offer bigger context not only to annotators but also to the model it trains. As a result, it helps ML developers to enhance the performance of the network with various techniques such as Kalman and temporal filters.
Temporal filters and Kalman filters help the model to add information from the nearest frames for helping in making decisions. With the help of temporal filtering, misclassifications can be filtered out depending on the existence or non-existence of specific things in adjacent frames. Kalman filters on the other hand utilise information from frames nearby to assess the most likely base of an object in the following frame.
The benefits of annotating video vary largely and hence when you choose the mode of annotation and data collection as video, you can expect the best quality of data annotation. Besides, the operation of annotation runs efficiently. Many advanced tools assist annotators to obtain these benefits and transform the preparing process of data into a great source of asset and not a burden.
Recurrent neural network (RNN) and long short-term memory (LSTM) network architectures means a wide spectrum of network architectures that help temporal components to consume and follow time series video that includes video. As they run on data using a temporal component, an accurate video annotation is highly beneficial for the training and deployment of LSTM or RNN architecture.