Real-Time RGB-D based Template Matching Pedestrian Detection

Pedestrian detection is one of the most popular topics in computer vision and robotics. Considering challenging issues in multiple pedestrian detection, we present a real-time depth-based template matching people detector. In this paper, we propose different approaches for training the depth-based template. We train multiple templates for handling issues due to various upper-body orientations of the pedestrians and different levels of detail in depth-map of the pedestrians with various distances from the camera. And, we take into account the degree of reliability for different regions of sliding window by proposing the weighted template approach. Furthermore, we combine the depth-detector with an appearance based detector as a verifier to take advantage of the appearance cues for dealing with the limitations of depth data. We evaluate our method on the challenging ETH dataset sequence. We show that our method outperforms the state-of-the-art approaches.Comment: published in ICRA 201

Multispectral Deep Neural Networks for Pedestrian Detection

Multispectral pedestrian detection is essential for around-the-clock applications, e.g., surveillance and autonomous driving. We deeply analyze Faster R-CNN for multispectral pedestrian detection task and then model it into a convolutional network (ConvNet) fusion problem. Further, we discover that ConvNet-based pedestrian detectors trained by color or thermal images separately provide complementary information in discriminating human instances. Thus there is a large potential to improve pedestrian detection by using color and thermal images in DNNs simultaneously. We carefully design four ConvNet fusion architectures that integrate two-branch ConvNets on different DNNs stages, all of which yield better performance compared with the baseline detector. Our experimental results on KAIST pedestrian benchmark show that the Halfway Fusion model that performs fusion on the middle-level convolutional features outperforms the baseline method by 11% and yields a missing rate 3.5% lower than the other proposed architectures.Comment: 13 pages, 8 figures, BMVC 2016 ora

Array joint detection for C/TDMA systems in UMTS environments

Two array-based schemes for intracell and intercell interference suppression are proposed. In both cases, the spatial and temporal characteristics of the propagation are jointly exploited by placing a narrowband beamformer prior to the corresponding data detection stage. In the first approach, the filtered training sequence joint detection receiver (FTS-JDR), the beamformer is devoted to exclusively cancel out intercell interference. This way, intracell users can be jointly detected in a MMSE detection block. In contrast, the second algorithm, known as the filtered training sequence multisensor receiver (FTS-MR), aims to attenuate all the interferers in the beamforming stage which allows the user of interest to be detected following a MLSE strategy. In order to assess the performance of the proposed schemes, a set of link-level computer simulations adopting FRAMES' proposal for UMTS air-interface as well as realistic channel models for third generation communication systems is provided. Simulation results indicate that lower BERs can be obtained by concentrating interference cancellation tasks in the beamforming block.Peer ReviewedPostprint (published version

Taking a look at small-scale pedestrians and occluded pedestrians

Small-scale pedestrian detection and occluded pedestrian detection are two challenging tasks. However, most state-of-the-art methods merely handle one single task each time, thus giving rise to relatively poor performance when the two tasks, in practice, are required simultaneously. In this paper, it is found that small-scale pedestrian detection and occluded pedestrian detection actually have a common problem, i.e., an inaccurate location problem. Therefore, solving this problem enables to improve the performance of both tasks. To this end, we pay more attention to the predicted bounding box with worse location precision and extract more contextual information around objects, where two modules (i.e., location bootstrap and semantic transition) are proposed. The location bootstrap is used to reweight regression loss, where the loss of the predicted bounding box far from the corresponding ground-truth is upweighted and the loss of the predicted bounding box near the corresponding ground-truth is downweighted. Additionally, the semantic transition adds more contextual information and relieves semantic inconsistency of the skip-layer fusion. Since the location bootstrap is not used at the test stage and the semantic transition is lightweight, the proposed method does not add many extra computational costs during inference. Experiments on the challenging CityPersons and Caltech datasets show that the proposed method outperforms the state-of-the-art methods on the small-scale pedestrians and occluded pedestrians (e.g., 5.20% and 4.73% improvements on the Caltech)

Performance evaluation of interference cancellation techniques using adaptive antennas

Two array-based algorithms, which jointly exploit or compensate for the spatial and temporal characteristics of the propagation channel, are proposed for intercell interference suppression in UMTS scenarios. The first one is the array extension of the Viterbi algorithm and is referred to as Vector Viterbi algorithm (VVA). The second algorithm, known as filtered training sequence multisensor receiver (FTS-MR), belongs to a class of algorithms in which a narrowband beamformer is placed prior to the MLSE detector. In order to assess performance of the proposed schemes, a set of link-level computer simulations adopting FRAMES' proposal for UMTS air-interface as well as realistic channel models for third generation communication systems is provided, Simulation results reveal gains, in terms of C/I, of 7-10 dB for the VVA with respect to the conventional VA and even higher for the FTS-MR.Peer ReviewedPostprint (published version