While generic object detection has achieved large improvements with rich feature hierarchies from deep nets, detecting small objects with poor visual cues remains challenging. Motion cues from multiple frames may be more informative for detecting such hard-to-distinguish objects in each frame. However, how to encode discriminative motion patterns, such as deformations and pose changes that characterize objects, has remained an open question. To learn them and thereby realize small object detection, we present a neural model called the Recurrent Correlational Network, where detection and tracking are jointly performed over a multi-frame representation learned through a single, trainable, and end-to-end network. A convolutional long short-term memory network is utilized for learning informative appearance change for detection, while learned representation is shared in tracking for enhancing its performance. In experiments with datasets containing images of scenes with small flying objects, such as birds and unmanned aerial vehicles, the proposed method yielded consistent improvements in detection performance over deep single-frame detectors and existing motion-based detectors. Furthermore, our network performs as well as state-of-the-art generic object trackers when it was evaluated as a tracker on the bird dataset.
Small object detection 1.5mm Detection of small objects has been tackled in the surveillance community @cite_49 , and recently has attracted much attention since the advent of UAVs @cite_50 @cite_35 . Small pedestrians @cite_61 and faces @cite_62 have also been considered, and some recent studies try to detect small common objects in generic-object detection setting @cite_22 @cite_3 . Studies are more focused on scale-tuned convnets with moderate depths and a wider field of view, and despite of its importance, motion has not yet been incorporated in these domains.
@cite_61 @cite_35 @cite_62 @cite_22 @cite_3 @cite_50 @cite_49
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Pedestrian detection is a well-studied problem. Even though many datasets contain challenging case studies, the performances of new methods are often only reported on cases of reasonable difficulty. In particular, the issue of small scale pedestrian detection is seldom considered. In this paper, we focus on the detection of small scale pedestrians, i.e., those that are at far distance from the camera. We show that classical features used for pedestrian detection are not well suited for our case of study. Instead, we propose a convolutional neural network based method to learn the features with an end-to-end approach. Experiments on the Caltech Pedestrian Detection Benchmark showed that we outperformed existing methods by more than 10 in terms of log-average miss rate. Though tremendous strides have been made in object recognition, one of the remaining open challenges is detecting small objects. We explore three aspects of the problem in the context of finding small faces: the role of scale invariance, image resolution, and contextual reasoning. While most recognition approaches aim to be scale-invariant, the cues for recognizing a 3px tall face are fundamentally different than those for recognizing a 300px tall face. We take a different approach and train separate detectors for different scales. To maintain efficiency, detectors are trained in a multi-task fashion: they make use of features extracted from multiple layers of single (deep) feature hierarchy. While training detectors for large objects is straightforward, the crucial challenge remains training detectors for small objects. We show that context is crucial, and define templates that make use of massively-large receptive fields (where 99 of the template extends beyond the object of interest). Finally, we explore the role of scale in pre-trained deep networks, providing ways to extrapolate networks tuned for limited scales to rather extreme ranges. We demonstrate state-of-the-art results on massively-benchmarked face datasets (FDDB and WIDER FACE). In particular, when compared to prior art on WIDER FACE, our results reduce error by a factor of 2 (our models produce an AP of 82 while prior art ranges from 29-64 ). Existing object detection literature focuses on detecting a big object covering a large part of an image. The problem of detecting a small object covering a small part of an image is largely ignored. As a result, the state-of-the-art object detection algorithm renders unsatisfactory performance as applied to detect small objects in images. In this paper, we dedicate an effort to bridge the gap. We first compose a benchmark dataset tailored for the small object detection problem to better evaluate the small object detection performance. We then augment the state-of-the-art R-CNN algorithm with a context model and a small region proposal generator to improve the small object detection performance. We conduct extensive experimental validations for studying various design choices. Experiment results show that the augmented R-CNN algorithm improves the mean average precision by 29.8 over the original R-CNN algorithm on detecting small objects. Small drones are a rising threat due to their possible misuse for illegal activities, in particular smuggling and terrorism. The project SafeShore, funded by the European Commission under the Horizon 2020 program, has launched the “drone-vs-bird detection challenge” to address one of the many technical issues arising in this context. The goal is to detect a drone appearing at some point in a video where birds may be also present: the algorithm should raise an alarm and provide a position estimate only when a drone is present, while not issuing alarms on birds. This paper reports on the challenge proposal, evaluation, and results1. Abstract : Under the three-year Video Surveillance and Monitoring (VSAM) project, the Robotics Institute at Carnegie Mellon University (CMU) and the Sarnoff Corporation have developed a system for autonomous Video Surveillance and Monitoring. The technical approach uses multiple, cooperative video sensors to provide continuous coverage of people and vehicles in a cluttered environment. This final report presents an overview of the system, and of the technical accomplishments that have been achieved. Details can be found in a set of previously published papers that together comprise Appendix A.