Lost and found: Identifying objects in long-term surveillance videos

Saemi, Mohamad Mahdi; See, John; Tan, Suyin.

doi:10.1109/icsipa.2015.7412171

Cited by 3 publications

(3 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, they evaluated proposed system on challenging dataset. Author in [7] proposes a bridge between classical object detection techniques. As literature describes in real world we have huge collection of surveillance videos.…”

Section: Research Trends In Surveillance and Sousveillancementioning

confidence: 99%

Research Trends in Surveillance through Sousveillance

Munir¹,

Imran²

2019

IJACSA

View full text Add to dashboard Cite

Collective Intelligence is an immense research area that has wide application to cross-disciplines, like social, legal, and computation. Research trends in Surveillance find its place in the work of this area generating curated data set helpful in answering complex queries. Sousveillance is a recent term coined by researchers and had been discussed in different literatures. However our findings suggest that integration of Surviellance through Sousviellance data set has not been given much importance in collective fashion. In this literature we introduced an effective model of collective intelligence by integrating surveillance through sousviellance in a campus environment. For testbed networking devices are used to generate sousvillance data to provide validation, and cleaning to enable reliability and trust in the target object.

show abstract

Section: Research Trends In Surveillance and Sousveillancementioning

confidence: 99%

Research Trends in Surveillance through Sousveillance

Munir¹,

Imran²

2019

IJACSA

View full text Add to dashboard Cite

show abstract

“…Due to the lack of ground truth labels, prediction of abnormal tracks was performed using synthetically generated anomalous tracks. A subsequent work [12] performed object classification on objects extracted from LOST videos for more than 23,000 frames under a variety of weather conditions. Zen et al [9] proposed a pixel-wise approach to determine the density of traffic captured for a whole month in New York City.…”

Section: Related Workmentioning

confidence: 99%

“…Secondly, we proposed two approaches for predicting anomalies at the day-level granularity: a trajectory-based statistical approach that calculates statistical difference with distance between trajectories and a time series-based classification approach that computes statistical difference in terms of number of trajectories per day. The statistical approach is motivated by the work in [12] which used trajectory-based information to predict abnormal trajectories, while time-series based approach allows daily trajectory information to be represented temporally. We report the insights obtained from these proposed schemes by experimenting with one-year data from a single camera of the LOST dataset.…”

Section: Introductionmentioning

confidence: 99%

Lost in Time: Temporal Analytics for Long-Term Video Surveillance

Khor

See

2018

Lecture Notes in Electrical Engineering

Self Cite

View full text Add to dashboard Cite

Video surveillance is a well researched area of study with substantial work done in the aspects of object detection, tracking and behavior analysis. With the abundance of video data captured over a long period of time, we can understand patterns in human behavior and scene dynamics through data-driven temporal analytics. In this work, we propose two schemes to perform descriptive and predictive analytics on long-term video surveillance data. We generate heatmap and footmap visualizations to describe spatially pooled trajectory patterns with respect to time and location. We also present two approaches for anomaly prediction at the day-level granularity: a trajectory-based statistical approach, and a time-series based approach. Experimentation with one year data from a single camera demonstrates the ability to uncover interesting insights about the scene and to predict anomalies reasonably well.

show abstract

Towards the Modelling of Veillance based Citizen Profiling using Knowledge Graphs

Munir

Jami

Wasi

2021

Open Computer Science

View full text Add to dashboard Cite

In this work we have proposed a model for Citizen Profiling. It uses veillance (Surveillance and Sousveillance) for data acquisition. For representation of Citizen Profile Temporal Knowledge Graph has been used through which we can answer semantic queries. Previously, most of the work lacks representation of Citizen Profile and have used surveillance for data acquisition. Our contribution is towards enriching the data acquisition process by adding sousveillance mechanism and facilitating semantic queries through representation of Citizen Profiles using Temporal Knowledge Graphs. Our proposed solution is storage efficient as we have only stored data logs for Citizen Profiling instead of storing images, audio, and video for profiling purposes. Our proposed system can be extended to Smart City, Smart Traffic Management, Workplace profiling etc. Agent based mechanism can be used for data acquisition where each Citizen has its own agent. Another improvement can be to incorporate a decentralized version of database for maintaining Citizen profile.

show abstract

Lost and found: Identifying objects in long-term surveillance videos

Cited by 3 publications

References 24 publications

Research Trends in Surveillance through Sousveillance

Research Trends in Surveillance through Sousveillance

Lost in Time: Temporal Analytics for Long-Term Video Surveillance

Towards the Modelling of Veillance based Citizen Profiling using Knowledge Graphs

Contact Info

Product

Resources

About