Generalized Kernel-based Visual Tracking

In this work we generalize the plain MS trackers and attempt to overcome standard mean shift trackers' two limitations. It is well known that modeling and maintaining a representation of a target object is an important component of a successful visual tracker. However, little work has been done on building a robust template model for kernel-based MS tracking. In contrast to building a template from a single frame, we train a robust object representation model from a large amount of data. Tracking is viewed as a binary classification problem, and a discriminative classification rule is learned to distinguish between the object and background. We adopt a support vector machine (SVM) for training. The tracker is then implemented by maximizing the classification score. An iterative optimization scheme very similar to MS is derived for this purpose.Comment: 12 page

Estimation of Average Car Speed Using the Haar-Like Feature and Correlation Tracker Method

The speed of a car traveling on the road can generally be estimated by using a speed gun. Efforts are needed to use CCTV (closed circuit television) as a tool that can be used to estimate the speed of the car so as to ease the burden on the road operator to estimate the speed of the car. This study discusses the estimated average speed of the car with the Haar-like Feature method used to detect the car, then the detection results are tracked using Correlatin Tracker to track the movement of objects that have been detected and calculate the distance of movement from the car, so that the speed of the car detected in video can be estimated. The results of the estimated average speed compared with the results of taking speed with a speed gun so that an error is obtained by MAE testing of 5,55 km / hour and the resulting standard deviation is 4,61 km / hour, thus it can be concluded that the system is made valid and can be used by road organizers to monitor the average speed of a car

Human Action Localization And Recognition In Unconstrained Videos

As imaging systems become ubiquitous, the ability to recognize human actions is becoming increasingly important. Just as in the object detection and recognition literature, action recognition can be roughly divided into classification tasks, where the goal is to classify a video according to the action depicted in the video, and detection tasks, where the goal is to detect and localize a human performing a particular action. A growing literature is demonstrating the benefits of localizing discriminative sub-regions of images and videos when performing recognition tasks. In this thesis, we address the action detection and recognition problems. Action detection in video is a particularly difficult problem because actions must not only be recognized correctly, but must also be localized in the 3D spatio-temporal volume. We introduce a technique that transforms the 3D localization problem into a series of 2D detection tasks. This is accomplished by dividing the video into overlapping segments, then representing each segment with a 2D video projection. The advantage of the 2D projection is that it makes it convenient to apply the best techniques from object detection to the action detection problem. We also introduce a novel, straightforward method for searching the 2D projections to localize actions, termed TwoPoint Subwindow Search (TPSS). Finally, we show how to connect the local detections in time using a chaining algorithm to identify the entire extent of the action. Our experiments show that video projection outperforms the latest results on action detection in a direct comparison. Second, we present a probabilistic model learning to identify discriminative regions in videos from weakly-supervised data where each video clip is only assigned a label describing what action is present in the frame or clip. While our first system requires every action to be manually outlined in every frame of the video, this second system only requires that the video be given a single highlevel tag. From this data, the system is able to identify discriminative regions that correspond well iii to the regions containing the actual actions. Our experiments on both the MSR Action Dataset II and UCF Sports Dataset show that the localizations produced by this weakly supervised system are comparable in quality to localizations produced by systems that require each frame to be manually annotated. This system is able to detect actions in both 1) non-temporally segmented action videos and 2) recognition tasks where a single label is assigned to the clip. We also demonstrate the action recognition performance of our method on two complex datasets, i.e. HMDB and UCF101. Third, we extend our weakly-supervised framework by replacing the recognition stage with a twostage neural network and apply dropout for preventing overfitting of the parameters on the training data. Dropout technique has been recently introduced to prevent overfitting of the parameters in deep neural networks and it has been applied successfully to object recognition problem. To our knowledge, this is the first system using dropout for action recognition problem. We demonstrate that using dropout improves the action recognition accuracies on HMDB and UCF101 datasets

People counting using an overhead fisheye camera

As climate change concerns grow, the reduction of energy consumption is seen as one of many potential solutions. In the US, a considerable amount of energy is wasted in commercial buildings due to sub-optimal heating, ventilation and air conditioning that operate with no knowledge of the occupancy level in various rooms and open areas. In this thesis, I develop an approach to passive occupancy estimation that does not require occupants to carry any type of beacon, but instead uses an overhead camera with fisheye lens (360 by 180 degree field of view). The difficulty with fisheye images is that occupants may appear not only in the upright position, but also upside-down, horizontally and diagonally, and thus algorithms developed for typical side-mounted, standard-lens cameras tend to fail. As the top-performing people detection algorithms today use deep learning, a logical step would be to develop and train a new neural-network model. However, there exist no large fisheye-image datasets with person annotations to facilitate training a new model. Therefore, I developed two people-counting methods that leverage YOLO (version 3), a state-of-the-art object detection method trained on standard datasets. In one approach, YOLO is applied to 24 rotated and highly-overlapping windows, and the results are post-processed to produce a people count. In the other approach, regions of interest are first extracted via background subtraction and only windows that include such regions are supplied to YOLO and post-processed. I carried out extensive experimental evaluation of both algorithms and showed their superior performance compared to a benchmark method

A Scheme for the Detection and Tracking of People Tuned for Aerial Image Sequences

Abstract. This paper addresses the problem of detecting and tracking a large number of individuals in aerial image sequences that have been taken from high altitude. We propose a method which can handle the numerous challenges that are associated with this task and demonstrate its quality on several test sequences. Moreover this paper contains several contributions to improve object detection and tracking in other domains, too. We show how to build an effective object detector in a flexible way which incorporates the shadow of an object and enhanced features for shape and color. Furthermore the performance of the detector is boosted by an improved way to collect background samples for the classifier train-ing. At last we describe a tracking-by-detection method that can handle frequent misses and a very large number of similar objects