Visual surveillance is an attempt to detect, recognize and track certain objects from image sequences, and more generally to understand and describe object behaviors.
Motivation for Surveillance Systems
The ability to learn what normal behaviors and anomalies are.
For example it is known that in an office building entrance, people usually go straight to the lobby after entering the building. Thus individuals that go in a different direction can be considered harmful.
Increases effectiveness of the entire surveillance process by paying attention only to certain events instead of watching and analyzing several different surveillance cameras as happens today. Such a system can also decrease costs.
Crowed Flux Statistics
For example, by knowing how many cars are using a certain road and how many are coming from road A or B we can decide which road to widen.
Blood Squirting Halloween skull
Just a nice usage found on coding4fun, not all reasons have to be dead serious.
Recognition of hand gestures in real time
Recognition of hand gestures in real time, based on neural networks (multilayer perceptron, Hopfield NN)
As part of the application occurs pattern recognition (hand gestures), taken with the camera. Hand position in the image is determined by the color of pixels hand. For pattern recognition using neural networks (multilayer perceptron, Hopfield neural network). Recognition occurs in real time
This screenshot shows the main window of the application, as well as the recognition result is shown
This shows the settings window colors and choice of color spaces.
This application demonstrates the usage of Image Processing Algorithms to Detect Human Skin in a picture, this can be used in a wide variety of AI projects.
This very simple application demonstrates self organizing feature of Kohonen artificial neural networks. Initially the application creates a neural network with neurons' weights initialized to coordinates of points in rectangular grid. After that the network is continuously fed by coordinates of previously generated random points. While these points are presented to the network, it does organization of its structure. Visualizing the neural network by treating neurons' weights as coordinates of points shows a picture, which is close to the picture of randomly generated map, which was fed to the network.
This application represents another sample showing self organization feature of Kohonen neural networks and building color clusters. All network's neurons have 3 inputs and initially 3 corresponding weights of each neuron are initialized randomly in the [0, 255] range. Weights of each neuron may be treated as RGB tuple, which means that initially neural network represents a rectangle of random colors. During training phase, the network is fed by random colors, which results to network's self organizing and forming color clusters.
This application demonstrates the usage of Neural Networks for predicting Market Share Values.
This "forecasting" capability makes them a perfect tool for several types of applications:
- Function interpolation and approximation
- Prediction of trends in numerical data
- Prediction of movements in financial markets
All the examples are actually very similar, because in mathematical terms, you are trying to define a prediction function F(X1, X2, ..., Xn), which according to the input data (vector [X1, X2, ..., Xn]), is going to "guess" (interpolate) the output Y. The most exciting domain of prediction lies in the field of financial market.