Video surveillance: past, present, and now the future [DSP Forum]

Porikli, Fatih; Brémond, François; Dockstader, S.L.; Ferryman, James; Hoogs, Anthony; Lovell, Brian C.; Pankanti, Sharath; Rinner, Bernhard; Tu, Peter; Venetianer, P.L.

doi:10.1109/msp.2013.2241312

Cited by 68 publications

(40 citation statements)

References 5 publications

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“…Today vision technology is successfully used in a wide variety of real-world applications, such as surveillance, robotics, industrial, medical, and entertainment systems [16]. A growing number of researchers are proposing to address the recognition of actions and objects with brain-inspired algorithms featuring multi-stage feature detectors and classifiers which can be customized using machine learning [6,7,11].…”

Section: Introductionmentioning

confidence: 99%

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Accelerating real-time embedded scene labeling with convolutional networks

Cavigelli

Magno

Benini

2015

Proceedings of the 52nd Annual Design Automation Conference

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Today there is a clear trend towards deploying advanced computer vision (CV) systems in a growing number of application scenarios with strong real-time and power constraints. Brain-inspired algorithms capable of achieving recordbreaking results combined with embedded vision systems are the best candidate for the future of CV and video systems due to their flexibility and high accuracy in the area of image understanding. In this paper, we present an optimized convolutional network implementation suitable for real-time scene labeling on embedded platforms. We show that our algorithm can achieve up to 96 GOp/s, running on the Nvidia Tegra K1 embedded SoC. We present experimental results, compare them to the state-of-the-art, and demonstrate that for scene labeling our approach achieves a 1.5x improvement in throughput when compared to a modern desktop CPU at a power budget of only 11 W.

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Section: Introductionmentioning

confidence: 99%

“…The term embedded vision is used to refer to such embedded systems that extract meaning from visual inputs at the sensor. It is clear that embedded vision technology can bring huge value to a vast range of applications, reducing data transmission by forwarding only the desired information [13,16].…”

Section: Introductionmentioning

confidence: 99%

Accelerating real-time embedded scene labeling with convolutional networks

Cavigelli

Magno

Benini

2015

Proceedings of the 52nd Annual Design Automation Conference

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“…State-of-the-art CNNs are demonstrating high performance and flexibility in a wide range of applications including video surveillance, object recognition and mobile robot vision [1]- [3]. In recent years there is a growing tendency to take advantage of large-scale and deep convolutional neural networks.…”

Section: Introductionmentioning

confidence: 99%

CNN-MERP: An FPGA-based memory-efficient reconfigurable processor for forward and backward propagation of convolutional neural networks

Han

Zhou

Wang

2016

2016 IEEE 34th International Conference on Computer Design (ICCD)

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Abstract-Large-scale deep convolutional neural networks (CNNs) are widely used in machine learning applications. While CNNs involve huge complexity, VLSI (ASIC and FPGA) chips that deliver high-density integration of computational resources are regarded as a promising platform for CNN's implementation. At massive parallelism of computational units, however, the external memory bandwidth, which is constrained by the pin count of the VLSI chip, becomes the system bottleneck. Moreover, VLSI solutions are usually regarded as a lack of the flexibility to be reconfigured for the various parameters of CNNs. This paper presents CNN-MERP to address these issues. CNN-MERP incorporates an efficient memory hierarchy that significantly reduces the bandwidth requirements from multiple optimizations including on/offchip data allocation, data flow optimization and data reuse. The proposed 2-level reconfigurability is utilized to enable fast and efficient reconfiguration, which is based on the control logic and the multiboot feature of FPGA. As a result, an external memory bandwidth requirement of 1.94MB/GFlop is achieved, which is 55% lower than prior arts. Under limited DRAM bandwidth, a system throughput of 1244GFlop/s is achieved at the Vertex UltraScale platform, which is 5.48 times higher than the state-of-the-art FPGA implementations.

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“…However, the raw data that the surveillance system collects has a common problem of its incompleteness [13]. It is mainly due to sensor's imperfect detection capability [10,20,22].…”

Section: Introductionmentioning

confidence: 99%

Trajectory based database management for intelligent surveillance system with heterogeneous sensors

Moon

Hong

2015

Multimed Tools Appl

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In this paper, we present a database management scheme for an intelligent surveillance system utilizing multiple visual sensors and RFID readers. The objects are tracked and identified by multiple visual sensors and RFID readers. We define three different types of data structure to consistently store data for effective data storage. They contain global object number and identification as the common information of the same object. The global object number is uniquely assigned for each track object. The previously stored data without the common information is back-annotated when it is available in the system. Moreover, when the global object number changes because of imperfect detection and tracking, the system maintains consistency information between global object numbers for the same object by comparing their local target information or positions. The fragmented information for an object is also stitched through map information. The simulation results demonstrate that the database information for objects is successfully recovered with consistency.

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Video surveillance: past, present, and now the future [DSP Forum]

Cited by 68 publications

References 5 publications

Accelerating real-time embedded scene labeling with convolutional networks

Accelerating real-time embedded scene labeling with convolutional networks

CNN-MERP: An FPGA-based memory-efficient reconfigurable processor for forward and backward propagation of convolutional neural networks

Trajectory based database management for intelligent surveillance system with heterogeneous sensors

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