How Deep Learning Algorithms are Used in Video Analytics

Deep learning techniques assist in detecting activity in a large crowd in real-time. Underlying deep learning implementation technology involved in various crowd video analysis methods. Real-time processing is an important issue that is yet to be explored more.

Deep learning has recently achieved promising results in a wide range of areas, including computer vision, speech recognition, and natural language processing. Unlike machine learning, deep learning aims to extract hierarchical representations from large scale data by using deep architecture models with multiple layers of non-linear transformations. Deep learning is capable of disentangling different levels of abstractions embedded in observed data. Since deep learning is suitable for handling large-scale data, it can be used to process and analyze millions of data captured from the distributed sensors.

Benefits of Intelligent Video Analysis in the Real World

The objectives identified illustrate the relevance of deep learning in Video Analytics solutions. The reasons are -

• Tiresome continuous monitoring of videos.
• Intelligent surveillance being a laborious task.
• Need for accuracy for object identification and action recognition.
• Requisite improvements in crowd analysis.
• Limiting the time taken to respond to the real-world situation.
• An effective solution such as predicting movement can save from violence.
• Availability of huge data in video forms.

Deep Learning Model in Surveillance

Three sections in the deep convolution framework can assist in detecting abnormal behavior -

• Human subject detection and discrimination
• A posture classification module
• An abnormal behavior detection module.

Detection of anomalous incidents in the video footage can be learned through deep representation and motion model, which is AMDN.

This model uses stacked de-noising auto-encoders, which helps in automatically learning feature representation. An autoencoder includes convolutional and pooling layers. The complete architecture allows the combination of low-level frames with high-level appearances and motion features.

Later, a Support Vector Machine (SVM) is trained, which predicts the score of each peculiarity input. Here the double fusion framework comes into play that combines all the scores to detect the abnormal event.

Besides methodology breakthroughs and available training data, the recent success of deep learning is due to the advances in the hardware. Especially an electronic unit that accelerates the algorithm needed to process a large number of blocks of data.

Reviewing the State of Art Applied in Deep Learning Algorithms

Object Detection

The aim here is to detect an object precisely in the video frames. Within this feature, one can detect pedestrians, on road-vehicles, unattended objects. Deep learning algorithms in video analytics are designed to handle large variations of different objects.

Object Tracking

Object tracking is meant to locate an object targeted in a video sequence for a given location. Object tracking is important to automatically track the suspected vehicles or people for safety management. As aforementioned, with stacked denoising autoencoder, deep learning-based tracking algorithms have achieved some promising results.

In order to learn discriminative feature representations of visual tracking, one of the best methods is through convolutional neural networks. In deep learning systems, a pool of convolutional neural networks is utilized to maintain different kernels. The low-level cues aim to discriminate object patches.

Deep learning can also be implemented to learn both complicated motion transformation and target appearance changes. The complicated motion transformations are learned from the auxiliary video data through a two-layer neural network.

As per the specific target object sequence, the features are pre-learned, which results in capturing the changes in the appearance of the target objects. This dictates that feature learning algorithms can significantly improve tracking performance, especially on the sequences with complex motion transformations.

Face Recognition

The task of face recognition simple - to identify and verify face. The face verification algorithm determines whether any face identified matches the same person targeting. The face identification algorithm recognizes the given face matching the known face set.

Application of Deep hidden Identity for face recognition is effective due to its super neural networks learning capacity.

Deep learning can utilize big data for training deep architecture models to obtain more powerful features for representing faces. The hidden features are shared by adding a robust regularization.

Face recognition can be used in security systems and human-machine interaction systems with Deep ID for near to human performance.

Image Classification

This feature has always been a hot topic. However, this feature too has delivered promising results with the assistance of deep learning in image classification. The deep model consists of five convolutional layers containing max-pooling layers with three fully connected layers.

The deep convolutional neural network has 60 million parameters and 650,000 neurons, which can capture data-driven representations for specific input data. The architecture delivered impressive results by reducing the error rates by 8%.

To Conclude -

The deep learning models in Video Analytics Singapore stimulates the delivery of near to accurate object detection, object tracking, face recognition, face recognition. The availability of big training data and new advanced hardware will build up large deep neural networks and reduce the training time for deep networks. We may look forward to having a better prospective of deep learning in a wide range of applications