Identity verification using computer vision for automatic garage door opening

We present a novel system for automatic identification of vehicles as part of an intelligent access control system for a garage entrance. Using a camera in the door, cars are detected and matched to the database of authenticated cars. Once a car is detected, License Plate Recognition (LPR) is applied using character detection and recognition. The found license plate number is matched with the database of authenticated plates. If the car is allowed access, the door will open automatically. The recognition of both cars and characters (LPR) is performed using state-ofthe- art shape descriptors and a linear classifier. Experiments have revealed that 90% of all cars are correctly authenticated from a single image only. Analysis of the computational complexity shows that an embedded implementation allows user authentication within approximately 300ms, which is well within the application constraints

Unsupervised sub-categorization for object detection: Finding cars from a driving vehicle

We present a novel algorithm for unsupervised subcategorization of an object class, in the context of object detection. Dividing the detection problem into smaller subproblems simplifies the object vs. background classification. The algorithm uses an iterative split-and-merge procedure and uses both object and non-object information. Sub-classes are automatically split into two new classes if the visual variation is too large, while two classes are merged if they are visually similar. After each iteration, samples are relabeled to the most preferred subclasses. In contrast to existing literature on unsupervised sub- categorization, our approach does not fix the number of final subclasses and determines this number using a visual similarity measure. Because we use a fast stochastic learning algorithm, full retraining and relabeling can be applied at each iteration. We show that the algorithm significantly outperforms state-of-the-art multi-class algorithms for a car detection problem using standard HOG features and simple linear classification, while significantly decreasing training time to a few minutes. Additionally, for our car detection problem, the identified subclasses by the algorithm were semantically meaningful and reveal the viewpoint of the object without the use of any motion information

Unsupervised sub-categorization for object detection: fInding cars from a driving vehicle

We present a novel algorithm for unsupervised subcategorization of an object class, in the context of object detection. Dividing the detection problem into smaller subproblems simplifies the object vs. background classification. The algorithm uses an iterative split-and-merge procedure and uses both object and non-object information. Sub-classes are automatically split into two new classes if the visual variation is too large, while two classes are merged if they are visually similar. After each iteration, samples are relabeled to the most preferred subclasses. In contrast to existing literature on unsupervised sub- categorization, our approach does not fix the number of final subclasses and determines this number using a visual similarity measure. Because we use a fast stochastic learning algorithm, full retraining and relabeling can be applied at each iteration. We show that the algorithm significantly outperforms state-of-the-art multi-class algorithms for a car detection problem using standard HOG features and simple linear classification, while significantly decreasing training time to a few minutes. Additionally, for our car detection problem, the identified subclasses by the algorithm were semantically meaningful and reveal the viewpoint of the object without the use of any motion information

3D wire-frame object-modeling experiments for video surveillance

We consider model-based object detection for traffic surveillance, aiming at ob ject classification. Within detected regions-of-interest (ROIs) of moving objects in the scene, the orientation of the object is detected using a histogram of gradi ent directions. For the calculated orientation, a 3D wire-framc model is projected onto the image data and the best matching pixel-position is calculated inside the ROT of the object

Identity verification using computer vision for automatic garage door opening

We present a novel system for automatic identification of vehicles as part of an intelligent access control system for a garage entrance. Using a camera in the door, cars are detected and matched to the database of authenticated cars. Once a car is detected, License Plate Recognition (LPR) is applied using character detection and recognition. The found license plate number is matched with the database of authenticated plates. If the car is allowed access, the door will open automatically. The recognition of both cars and characters (LPR) is performed using state-ofthe- art shape descriptors and a linear classifier. Experiments have revealed that 90% of all cars are correctly authenticated from a single image only. Analysis of the computational complexity shows that an embedded implementation allows user authentication within approximately 300ms, which is well within the application constraints

Comparing feature matching for visual object categorization: MAX vs. bag-of-words

In this paper we address the comparison of two feature matching techniques which can be integrated in the HMAX framework. This comparison involves the originally proposed MAX technique and the histogram technique originating from Bag-of-Words literature. We have found that each of these techniques have their own field of operation. The histogram technique clearly outperforms the MAX technique with 5-15% for small dictionaries up to 500--1,000 features. A second investigation concentrates on comparing the often used hard vector quantization technique and a soft matching score technique for the histogram creation. It was found that the difference in performance is not significant and the scores are often within their standard deviations. Aiming at an embedded implementation such as in a surveillance system, computation power and memory (number of dictionary features) are intrinsically limited, so that the histogram technique is favored over the MAX technique

TROD: Tracking with occlusion handling and drift correction

We present a tracking framework in which we learn a HOG-based object detector in the first video frame and use this detector to localize the object in subsequent frames. We contribute and improve the tracking on the three following points. First, an occlusion-handling algorithm exploits discriminative information from the detector by dividing the object bounding box into patches and comparing each patch to the object model. Second, a drift-correction technique uses descriptive information of the object by calculating the similarity between the object in the previous frame and its shifted versions in the current frame. Third, a stochastic learning algorithm updates the object detector using single object and single background samples for selected frames only. Experiments with benchmark sequences show that the proposed tracker outperforms state-of-the-art methods on several sequences and has the smallest average location error

Robust automatic ship tracking in harbours using active cameras

Radar is commonly used to detect and track ships in maritime surveillance. Unfortunately the systems are costly and do not provide any visual information about the object's type. To complement the ship identity information given by a radar system, we propose a supplementary system using active visual cameras that can robustly detect and track ships in harbours. By combining a high-quality, non real-time robust object detector with a feature point tracker with low computational complexity, it is possible to track ships in real time over long intervals and large distances. In addition to controlling pan and tilt, we dynamically control camera zoom to provide a high resolution image of the tracked object over a large range of distances. The tracking system is improved by a special motion estimation model for the feature points, which also incorporates zooming of the camera. The system is robust and sustains tracking even under challenging conditions, such as multiple viewpoints, a large variety of ships and various weather conditions. During experiments, various types of ships were successfully tracked for up to 18 minutes, and over a distance of almost 1.5km in the port of Rotterdam. The proposed system is generic and can be utilized in various tracking applications, by training the detector for a different object class

Applying feature selection techniques for visual dictionary creation in object classification

This paper introduces improved methods for visual dictionary creation in an object classification system. In literature, the visual dictionary is often created from a large candidate set of features by random selection or by a clustering algorithm. We apply techniques from feature selection literature to create a more optimal visual dictionary and contribute with a novel feature selection algorithm. As a second step, feature extraction techniques for creating the candidate set are investigated. Subsequently, the size of the candidate set is varied. It was found that the exploitation of feature selection techniques gives a clear improvement of 2-5% in classification rate at no additional computational cost in normal system operation. The proposed algorithm called extremal optimization, outperforms state-of-the-art algorithms. The paper discloses results on candidate set creation using interest point operators. As a general bonus, the evaluated feature selection techniques are generally applicable to any problem that uses a dictionary of features, as typically applied in the object recognition domain