I was wondering if anyone has managed to use the OpenCV implementation of Latent SVM Detector (http://docs.opencv.org/modules/objdetect/doc/latent_svm.html) successfully. There is a sample code that shows how to utilize the library but the problem is that the sample code uses a ready-made detector model that was generated using MatLab. Can some one guide me through the steps on how to generate my own detector model? 

Answer: 

The MATLAB implementation of LatSVM by the authors of the paper has a train script called pascal. There is a README with the tarball explaining its usage: 

 Using the learning code
=======================

1. Download and install the 2006-2011 PASCAL VOC devkit and dataset.
   (you should set VOCopts.testset='test' in VOCinit.m)
2. Modify 'voc_config.m' according to your configuration.
3. Start matlab.
4. Run the 'compile' function to compile the helper functions.
   (you may need to edit compile.m to use a different convolution 
    routine depending on your system)
5. Use the 'pascal' script to train and evaluate a model. 

example:
>> pascal('bicycle', 3);   % train and evaluate a 6 component bicycle model

The learning code saves a number of intermediate models in a model cache
directory defined in 'voc_config.m'. 

OpenCV is an open-source computer vision and machine learning software library. It provides a wide range of tools and functions for image and video processing, including object detection. One of these object detection methods is the Latent SVM Detector.

To use the Latent SVM Detector in OpenCV, you will first need to install OpenCV on your computer. You can do this by following the installation instructions on the OpenCV website.

Once you have installed OpenCV, you can start using the Latent SVM Detector. The Latent SVM Detector uses a machine learning algorithm to detect objects in an image or video. To use the Latent SVM Detector, you need to provide a trained SVM model and an object detection function that uses the model to detect objects in an image.

The trained SVM model can be obtained by training the model on a large dataset of positive and negative samples. Once you have the trained SVM model, you can use the cv2.LatentSVMDetector_create() function in OpenCV to create an instance of the Latent SVM Detector.

To detect objects in an image, you can use the detect() method of the Latent SVM Detector. This method takes an image as an input and returns a list of detected objects and their locations in the image.

In conclusion, OpenCV and the Latent SVM Detector are powerful tools for object detection in images and videos. With OpenCV and the Latent SVM Detector, you can build computer vision applications that can recognize and locate objects in real-time.