Geometry-Aware Video Object Detection for Static Cameras

Abstract

In this paper we propose a geometry-aware model for video object detection. Specifically, we consider the setting that cameras can be well approximated as static, e.g. in video surveillance scenarios, and scene pseudo depth maps can therefore be inferred easily from the object scale on the image plane.We make the following contributions: First, we extend the recent anchor-free detector (CornerNet [17]) to video object detections. In order to exploit the spatial-temporal information while maintaining high efficiency, the proposed model accepts video clips as input, and only makes predictions for the starting and the ending frames, i.e. heatmaps of object bounding box corners and the corresponding embeddings for grouping. Second, to tackle the challenge from scale variations in object detection, scene geometry information, e.g. derived depth maps, is explicitly incorporated into deep networks for multiscale feature selection and for the network prediction. Third, we validate the proposed architectures on an autonomous driving dataset generated from the Carla simulator [5], and on a real dataset for human detection (DukeMTMC dataset [28]). When comparing with the existing competitive single-stage or two-stage detectors, the proposed geometryaware spatio-temporal network achieves significantly better results.

Recent Advances in Video Analytics for Rail Network Surveillance for Security, Trespass and Suicide Prevention—A Survey

“…Static object detection (SOD) [75][76][77] alarms a surveillance operator when an instance of an object of interest is detected in the scene captured by the cameras. The methods for static object detection are different from the moving object as there is no relative movement between the object of interest and the background.…”

Tiny-YOLO object detection supplemented with geometrical data

“…In the [2] paper authors work in similar setting, but they obtain the geometrical data from the video stream. In this work a static camera is used, which is not the case in the autonomous robots competitions.…”

Salient Target Detection Method of Video Images Based on Convolution Neural Network

“…Video image salient target detection is to simulate human visual perception system, intelligently detect salient targets in video images from semantic level, and finally realize independent analysis and understanding of video image content [5][6][7][8][9][10][11]. Traditional target detection of video images is often used to distinguish the relevant classification of large categories of targets, in the case of complex and diverse image content, it can not capture enough visual cues, which makes it difficult to distinguish small differences between categories [12][13][14][15][16][17][18][19][20][21][22]. To solve this problem, it's impossible to rely on all kinds of artificial image annotation to prompt which areas the detection model needs to extract which target feature information.…”