Carlos Caetano

A mid-level video representation based on binary descriptors: A case study for pornography detection

Abstract With the growing amount of inappropriate content on the Internet, such as pornography, arises the need to detect and filter such material. The reason for this is given by the fact that such content is often prohibited in certain environments (e.g., schools and workplaces) or for certain publics (e.g., children). In recent years, many works have been mainly focused on detecting pornographic images and videos based on visual content, particularly on the detection of skin color. Although these approaches provide good results, they generally have the disadvantage of a high false positive rate since not all images with large areas of skin exposure are necessarily pornographic images, such as people wearing swimsuits or images related to sports. Local feature based approaches with Bag-of-Words models (BoW) have been successfully applied to visual recognition tasks in the context of pornography detection. Even though existing methods provide promising results, they use local feature descriptors that require a high computational processing time yielding high-dimensional vectors. In this work, we propose an approach for pornography detection based on local binary feature extraction and BossaNova image representation, a BoW model extension that preserves more richly the visual information. Moreover, we propose two approaches for video description based on the combination of mid-level representations namely BossaNova Video Descriptor (BNVD) and BoW Video Descriptor (BoW-VD). The proposed techniques are promising, achieving an accuracy of 92.40%, thus reducing the classification error by 16% over the current state-of-the-art local features approach on the Pornography dataset.

Representing local binary descriptors with BossaNova for visual recognition

Binary descriptors have recently become very popular in visual recognition tasks. This popularity is largely due to their low complexity and for presenting similar performances when compared to non binary descriptors, like SIFT. In literature, many researchers have applied binary descriptors in conjunction with mid-level representations (e.g., Bag-of-Words). However, despite these works have demonstrated promising results, their main problems are due to use of a simple mid-level representation and the use of binary descriptors in which rotation and scale invariance are missing. In order to address those problems, we propose to evaluate state-of-the-art binary descriptors, namely BRIEF, ORB, BRISK and FREAK, in a recent mid-level representation, namely BossaNova, which enriches the Bag-of-Words model, while preserving the binary descriptor information. Our experiments carried out in the challenging PASCAL VOC 2007 dataset revealed outstanding performances. Also, our approach shows good results in the challenging real-world application of pornography detection.

Histograms of Optical Flow Orientation and Magnitude and Entropy to Detect Anomalous Events in Videos

This paper presents an approach for detecting anomalous events in videos with crowds. The main goal is to recognize patterns that might lead to an anomalous event. An anomalous event might be characterized by the deviation from the normal or usual, but not necessarily in an undesirable manner, e.g., an anomalous event might just be different from normal but not a suspicious event from the surveillance point of view. One of the main challenges of detecting such events is the difficulty to create models due to their unpredictability and their dependency on the context of the scene. Based on these challenges, we present a model that uses general concepts, such as orientation, velocity, and entropy to capture anomalies. Using such a type of information, we can define models for different cases and environments. Assuming images captured from a single static camera, we propose a novel spatiotemporal feature descriptor, called histograms of optical flow orientation and magnitude and entropy, based on optical flow information. To determine the normality or abnormality of an event, the proposed model is composed of training and test steps. In the training, we learn the normal patterns. Then, during test, events are described and if they differ significantly from the normal patterns learned, they are considered as anomalous. The experimental results demonstrate that our model can handle different situations and is able to recognize anomalous events with success. We use the well-known UCSD and Subway data sets and introduce a new data set, namely, Badminton.

Optical Flow Co-occurrence Matrices: A novel spatiotemporal feature descriptor

Suitable feature representation is essential for performing video analysis and understanding in applications within the smart surveillance domain. In this paper, we propose a novel spatiotemporal feature descriptor based on co-occurrence matrices computed from the optical flow magnitude and orientation. Our method, called Optical Flow Co-occurrence Matrices (OFCM), extracts a robust set of measures known as Haralick features to describe the flow patterns by measuring meaningful properties such as contrast, entropy and homogeneity of co-occurrence matrices to capture local space-time characteristics of the motion through the neighboring optical flow magnitude and orientation. We evaluate the proposed method on the action recognition problem by applying a visual recognition pipeline involving bag of local spatiotemporal features and SVM classification. The experimental results, carried on three well-known datasets (KTH, UCF Sports and HMDB51), demonstrate that OFCM outperforms the results achieved by several widely employed spatiotemporal feature descriptors such as HOF, HOG3D and MBH, indicating its suitability to be used as video representation.

Video similarity search by using compact representations

The amount of applications using unstructured data, like videos, has been increased, and the researches concerning multimedia retrieval have attracted great attention. The need to efficiently index and retrieve this kind of data is of great concern, due to the fact that common searching approaches based on the use of keywords are not adequate for large video databases. Similarity search is a content based approach and it has been successfully used in retrieval systems. Accordingly, a major challenge is to provide an accurate and compact video representation that can achieve good performance with a fast answer in this type of searching. In this work, we proposed a compact video representation by using Min-Hash and the k-nearest GIST descriptors. Furthermore, we also present the first use of BossaNova Video Descriptor (BNVD) to video similarity search. Both compact video representations have shown more than 88% of mean average precision on similarity video search. The experimental results indicate high efficiency of our proposed representations in video retrieval task.

Transmission line grounding arrangement that overcomes the effective length issue

Obtaining a low value for the transmission line grounding impedance is an important issue when dealing with backflashover performance. Usually, for high resistivity soils, increasing the grounding grid dimensions does not lead to low impedance due to the effective length issue. In this work, a special arrangement of ground electrodes is proposed in order to overcome the effective length problem in high resistivity soil. The transient voltage response of this low impedance ground arrangement is determined numerically using parametric modeling approach. Additionally, comparisons of numerical simulations results and measurements from a reduced model study are presented for validation purpose. Regarding the calculation, the grounding arrangement behavior is analyzed considering the frequency dependence of ground electrical parameters.

Novel Anomalous Event Detection based on Human-object Interactions.

This study proposes a novel approach to anomalous event detection that collects information from a specific context and is flexible enough to work in different scenes (i.e., the camera does need to be at the same location or in the same scene for the learning and test stages of anomaly event detection), making our approach able to learn normal patterns (i.e., patterns that do not entail an anomaly) from one scene and be employed in another as long as it is within the same context. For instance, our approach can learn the normal behavior for a context such the office environment by watching a particular office, and then it can monitor the behavior in another office, without being constrained to aspects such as camera location, optical flow or trajectories, as required by the current works. Our paradigm shift anomalous event detection approach exploits human-object interactions to learn normal behavior patterns from a specific context. Such patterns are used afterwards to detect anomalous events in a different scene. The proof of concept shown in the experimental results demonstrate the viability of two strategies that exploit this novel paradigm to perform anomaly detection.

Statistical Measures from Co-occurrence of Codewords for Action Recognition.

In this paper, we propose a novel spatiotemporal feature representation based on co-occurrence matrices of codewords, called Co-occurrence of Codewords (CCW), to tackle human action recognition, a significant problem for many real-world applications, such as surveillance, video retrieval and health care. The method captures local relationships among the codewords (densely sampled), through the computation of a set of statistical measures known as Haralick textural features. We apply a classical visual recognition pipeline in which involves the extraction of spatiotemporal features and SVM classification. We investigate the proposed representation in three well-known and publicly available datasets for action recognition (KTH, UCF Sports and HMDB51) and show that it outperforms the results achieved by several widely employed spatiotemporal features available in the literature encoded by a Bag-of-Words model with a more compact representation.

Experimental investigation of magnetic field shielding techniques and resulting current derating of underground power cables

The paper presents an experimental investigation on the efficiency of different underground transmission/distribution line magnetic shielding techniques. Measurement results of magnetic field and temperature rise around the cable conductors are shown. An analysis of the thermal effects of each shielding technique on the line power capacity is presented. The experiments were performed on a test site where a real 138-kV underground cable section was built. The results provide useful information that can guide the designer when choosing the most suitable shielding technique according to the required shielding factor taking into account its impact on the line-rated power.

Grounding resistance measurements using very short current and potential leads

The conductor length of the voltage and current circuits used in ground resistance measurements may be quite long depending on the grounding characteristics. Using the technique proposed in this paper, measurements are performed with very short conductors in both circuits. The current circuit is only 12 meters long and the voltage circuit varies from 4to8 meters. The technique is evaluated by comparisons with computational simulations and a good agreement between the results is verified.