In the field of out-of-distribution (OOD) detection, a previous method that use auxiliary data as OOD data has shown promising performance. However, the method provides an equal loss to all auxiliary data to differentiate them from inliers. However, based on our observation, in various tasks, there is a general imbalance in the distribution of the auxiliary OOD data across classes. We propose a balanced energy regularization loss that is simple but generally effective for a variety of tasks. Our balanced energy regularization loss utilizes class-wise different prior probabilities for auxiliary data to address the class imbalance in OOD data. The main concept is to regularize auxiliary samples from majority classes, more heavily than those from minority classes. Our approach performs better for OOD detection in semantic segmentation, long-tailed image classification, and image classification than the prior energy regularization loss. Furthermore, our approach achieves state-of-the-art performance in two tasks: OOD detection in semantic segmentation and long-tailed image classification.

This paper proposes a selective ensemble deep network architecture for crowd density estimation and people counting. In contrast to existing deep network-based methods, the proposed method incorporates two sub-networks for local density estimation: one to learn sparse density regions and one to learn dense density regions. Locally estimated density maps from the two sub-networks are selectively combined in ensemble fashion using a gating network to estimate an initial crowd density map. The initial density map is refined as a high resolution map, using another sub-network that draws on contextual information in the image. In training, a novel adaptive loss scheme is applied to resolve an ambiguity in the crowded region. the proposed scheme improves both density map accuracy and counting accuracy by adjusting the weighting value between density loss and counting loss according to the degree of crowdness and training epochs. Experiments using public datasets confirm that the proposed method outperforms state-of-the-art methods. Through self-evaluation, the effectiveness of each part in the network is also verified.

This paper proposes a novel path prediction algorithm for progressing one step further than the existing works focusing on single target path prediction. In this paper, we consider moving dynamics of co-occurring objects for path prediction in a scene that includes crowded moving objects. To solve this problem, we first suggest a two-layered probabilistic model to find major movement patterns and their cooccurrence tendency. By utilizing the unsupervised learning results from the model, we present an algorithm to find the future location of any target object. Through extensive qualitative/quantitative experiments, we show that our algorithm can find a plausible future path in complex scenes with a large number of moving objects.

In this paper, we propose a novel algorithm for 3D object cuboid detection. Contrary to the conventional algorithms based on image segmentation, we propose a part-based voting process to robustly generate cuboids when the object is partially occluded. Our method finds the distinctive parts of RGB-D images and generates the 3D cuboids covering the target objects from the distinctive parts by the proposed probabilistic voting model. To validate the performance of the proposed method, experiments are conducted on the challenging NYU v2 and SUN RGB-D datasets. Experimental results show that our method is computationally efficient and has a competitive performance compared with the state-of-the-art methods. In addition, our method can be combined with the conventional segmentation-based method parallelly, and the combined algorithm is evaluated by experiments to show that it achieves a significant improvement of performance.

In this paper, we propose a novel selective search method to speed up the object detection via category-based attention scheme. The proposed attentional searching strategy is designed to focus on a small set of selected regions where the object category is expected to exist. The selected regions are estimated by mimicking three properties of the attentional scheme of human visual perception: spotlighting interest regions with low-level saliency (saliency attention), focusing on distinctive features for an object category (feature attention), and estimating potential object position by following human gaze path (gaze attention). Also, the time complexity of each attentional scheme is implemented to be low so that it can hardly affect the computational time. To validate the performance of our method, experiments were conducted on the challenging PASCAL VOC dataset. Experimental results show that our method efficiently generates a small number of candidate boxes for object detection (less than 10ms=image), and the combined object detection system achieves more than 2 times faster performance than the baseline with comparable average precision.

This paper proposes a transfer learning scheme for traffic pattern analysis where the transferred classifier could be trained with a small number of samples. First we make feature descriptors to represent the traffic trajectories so that they should be adequate to transfer and classify the traffic patterns. Then, we use support vector machine (SVM) to learn the feature descriptors of traffic trajectories. The transfer learning scheme is formulated by a convex optimization problem using the geometric relation between target and source patterns. Not only parameters of SVM but also the geometric relation are found at the same time through two step minimization process of the optimization problem. Through experiments on various surveillance videos, the proposed formulation is shown to be valid by investigating the improvement of performance compared to a transfer scheme without the proposed geometric relation as well as SVM without transfer scheme.

In this paper, we propose a framework which self-organizes the cascaded object detection filters for fast object detection with maintaining high accuracy. The proposed scheme consists of root and part filter modules, which are cascaded in a self-organizing structure. The pruning of non-object regions in low resolution at the root cascade stage is critical for the object detection speed. At root stage, to prune as many non-object regions as possible, we build a root cascade structure using multiple root models. These models are obtained via bagging procedure for non-linear classification of object and non-object parts in an image. Additional speed-up is achieved by determining proper deployment order of part models. We define a discriminability measure for the part models and suggest a self-organizing scheme to generate an efficient order of part models. The proposed method is evaluated through computational experiments with the PASCAL VOC and INRIA datasets, as a result, our method achieves on average more than 2 times faster performance than the original cascade-DPM, with comparable precision scores.

This paper presents a novel method for modeling of interaction among multiple moving objects to detect traffic accidents. The proposed method to model object interactions is motivated by the motion of water waves responding to moving objects on water surface. The shape of the water surface is modeled in a field form using Gaussian kernels, which is referred to as the Motion Interaction Field (MIF). By utilizing the symmetric properties of the MIF, we detect and localize traffic accidents without solving complex vehicle tracking problems. Experimental results show that our method outperforms the existing works in detecting and localizing traffic accidents.

In this paper we propose an object tracking method in case of inaccurate initializations. To track objects accurately in such situation, the proposed method uses "motion saliency" and "descriptor saliency" of local features and performs tracking based on generalized Hough transform (GHT). The proposed motion saliency of a local feature emphasizes features having distinctive motions, compared to the motions which are not from the target object. The descriptor saliency emphasizes features which are likely to be of the object in terms of its feature descriptors. Through these saliencies, the proposed method tries to "learn and find" the target object rather than looking for what was given at initialization, giving robust results even with inaccurate initializations. Also, our tracking result is obtained by combining the results of each local feature of the target and the surroundings with GHT voting, thus is robust against severe occlusions as well. The proposed method is compared against nine other methods, with nine image sequences, and hundred random initializations. The experimental results show that our method outperforms all other compared methods.

In this paper, we present an active sampling method to speed up conventional pixel-wise background subtraction algorithms. The proposed active sampling strategy is designed to focus on attentional region such as foreground regions. The attentional region is estimated by detection results of previous frame in a recursive probabilistic way. For the estimation of the attentional region, we propose a foreground probability map based on temporal, spatial, and frequency properties of foregrounds. By using this foreground probability map, active attentional sampling scheme is developed to make a minimal sampling mask covering almost foregrounds. The effectiveness of the proposed active sampling method is shown through various experiments. The proposed masking method successfully speeds up pixel-wise background subtraction methods approximately 6.6 times without deteriorating detection performance. Also realtime detection with Full HD video is successfully achieved through various conventional background subtraction algorithms.

This paper proposes a trajectory analysis method by handling the spatio-temporal property of trajectory. Not using similarity measures of two trajectories, our model analyzes overall path of a trajectory. Learning of spatio property is presented as semantic regions (e.g. go straight, turn left, turn right) that are clustered effectively using topic model. The temporal order of observations on a trajectory is taken into account using HMM for detecting global anomaly. Results of experiments show that modeling of semantic region and detecting of unusual trajectories are successful even in complex scenes.

In this paper, we present a tracking failure detection method by imitating human visual system. By adopting log-polar transformation, we could simulate properties of retina image, such as rotation and scaling invariance and foveal predominance. The rotation and scaling invariance helps to reduce false alarms caused by pose changes and intensify translational changes. Foveal predominant property helps to detect the tracking failing moment by amplifying the resolution around focus (tracking box center) and blurring the peripheries. Each ganglion cell corresponds to a pixel of log-polar image, and its adaptation is modeled as Gaussian mixture model. Its validity is shown through various experiments.